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WE FACE a crisis of computing. The very devices that were supposed to augment our minds now harvest them for profit. How did we get here?

Most of us only know the oft-told mythology featuring industrious nerds who sparked a revolution in the garages of California. The heroes of the epic: Jobs, Gates, Musk, and the rest of the cast. Earlier this year, Mark Zuckerberg, hawker of neo-Esperantist bromides about “connectivity as panacea” and leader of one of the largest media distribution channels on the planet, excused himself by recounting to senators an “aw shucks” tale of building Facebook in his dorm room. Silicon Valley myths aren’t just used to rationalize bad behavior. These business school tales end up restricting how we imagine our future, limiting it to the caprices of eccentric billionaires and market forces.

What we need instead of myths are engaging, popular histories of computing and the internet, lest we remain blind to the long view.

At first blush, Yasha Levine’s Surveillance Valley: The Secret Military History of the Internet (2018) seems to fit the bill. A former editor of The eXile, a Moscow-based tabloid newspaper, and investigative reporter for PandoDaily, Levine has made a career out of writing about the dark side of tech. In this book, he traces the intellectual and institutional origins of the internet. He then focuses on the privatization of the network, the creation of Google, and revelations of NSA surveillance. And, in the final part of his book, he turns his attention to Tor and the crypto community.

He remains unremittingly dark, however, claiming that these technologies were developed from the beginning with surveillance in mind and that their origins are tangled up with counterinsurgency research in the Third World. This leads him to a damning conclusion: “The Internet was developed as a weapon and remains a weapon today.”

To be sure, these constitute provocative theses, ones that attempt to confront not only the standard Silicon Valley story, but also established lore among the small group of scholars who study the history of computing. He falls short, however, of backing up his claims with sufficient evidence. Indeed, he flirts with creating a mythology of his own — one that I believe risks marginalizing the most relevant lessons from the history of computing.

The scholarly history is not widely known and worth relaying here in brief. The internet and what today we consider personal computing came out of a unique, government-funded research community that took off in the early 1960s. Keep in mind that, in the preceding decade, “computers” were radically different from what we know today. Hulking machines, they existed to crunch numbers for scientists, researchers, and civil servants. “Programs” consisted of punched cards fed into room-sized devices that would process them one at a time. Computer time was tedious and riddled with frustration. A researcher working with census data might have to queue up behind dozens of other users, book time to run her cards through, and would only know about a mistake when the whole process was over.

Users, along with IBM, remained steadfast in believing that these so-called “batch processing” systems were really what computers were for. Any progress, they believed, would entail building bigger, faster, better versions of the same thing.

But that’s obviously not what we have today. From a small research, a community emerged an entirely different set of goals, loosely described as “interactive computing.” As the term suggests, using computers would no longer be restricted to a static one-way process but would be dynamically interactive. According to the standard histories, the man most responsible for defining these new goals was J. C. R. Licklider. A psychologist specializing in psychoacoustics, he had worked on early computing research, becoming a vocal proponent for interactive computing. His 1960 essay “Man-Computer Symbiosis” outlined how computers might even go so far as to augment the human mind.

It just so happened that funding was available. Three years earlier in 1957, the Soviet launch of Sputnik had sent the US military into a panic. Partially in response, the Department of Defense (DoD) created a new agency for basic and applied technological research called the Advanced Research Projects Administration (ARPA, today is known as DARPA). The agency threw large sums of money at all sorts of possible — and dubious — research avenues, from psychological operations to weather control. Licklider was appointed to head the Command and Control and Behavioral Sciences divisions, presumably because of his background in both psychology and computing.

At ARPA, he enjoyed relative freedom in addition to plenty of cash, which enabled him to fund projects in computing whose military relevance was decidedly tenuous. He established a nationwide, multi-generational network of researchers who shared his vision. As a result, almost every significant advance in the field from the 1960s through the early 1970s was, in some form or another, funded or influenced by the community he helped establish.

Its members realized that the big computers scattered around university campuses needed to communicate with one another, much as Licklider had discussed in his 1960 paper. In 1967, one of his successors at ARPA, Robert Taylor, formally funded the development of a research network called the ARPANET. At first the network spanned only a handful of universities across the country. By the early 1980s, it had grown to include hundreds of nodes. Finally, through a rather convoluted trajectory involving international organizations, standards committees, national politics, and technological adoption, the ARPANET evolved in the early 1990s into the internet as we know it.

Levine believes that he has unearthed several new pieces of evidence that undercut parts of this early history, leading him to conclude that the internet has been a surveillance platform from its inception.

The first piece of evidence he cites comes by way of ARPA’s Project Agile. A counterinsurgency research effort in Southeast Asia during the Vietnam War, it was notorious for its defoliation program that developed chemicals like Agent Orange. It also involved social science research and data collection under the guidance of an intelligence operative named William Godel, head of ARPA’s classified efforts under the Office of Foreign Developments. On more than one occasion, Levine asserts or at least suggests that Licklider and Godel’s efforts were somehow insidiously intertwined and that Licklider’s computing research in his division of ARPA had something to do with Project Agile. Despite arguing that this is clear from “pages and pages of released and declassified government files,” Levine cites only one such document as supporting evidence for this claim. It shows how Godel, who at one point had surplus funds, transferred money from his group to Licklider’s department when the latter was over budget.

This doesn’t pass the sniff test. Given the freewheeling nature of ARPA’s funding and management in the early days, such a transfer should come as no surprise. On its own, it doesn’t suggest a direct link in terms of research efforts. Years later, Taylor asked his boss at ARPA to fund the ARPANET — and, after a 20-minute conversation, he received $1 million in funds transferred from ballistic missile research. No one would seriously suggest that ARPANET and ballistic missile research were somehow closely “intertwined” because of this.

Sharon Weinberger’s recent history of ARPA, The Imagineers of War: The Untold Story of DARPA, The Pentagon Agency that Changed the World(2017), which Levine cites, makes clear what is already known from the established history. “Newcomers like Licklider were essentially making up the rules as they went along,” and were “given broad berth to establish research programs that might be tied only tangentially to a larger Pentagon goal.” Licklider took nearly every chance he could to transform his ostensible behavioral science group into an interactive computing research group. Most people in wider ARPA, let alone the DoD, had no idea what Licklider’s researchers were up to. His Command and Control division was even renamed the more descriptive Information Processing Techniques Office (IPTO).

Licklider was certainly involved in several aspects of counterinsurgency research. Annie Jacobsen, in her book The Pentagon’s Brain: An Uncensored History of DARPA, America’s Top-Secret Military Research Agency (2015), describes how he attended meetings discussing strategic hamlets in Southeast Asia and collaborated on proposals with others who conducted Cold War social science research. And Levine mentions Licklider’s involvement with a symposium that addressed how computers might be useful in conducting counterinsurgency work.

But Levine only points to one specific ARPA-funded computing research project that might have had something to do with counterinsurgency. In 1969, Licklider — no longer at ARPA — championed a proposal for a constellation of research efforts to develop statistical analysis and database software for social scientists. The Cambridge Project, as it was called, was a joint effort between Harvard and MIT. Formed at the height of the antiwar movement, when all DoD funding was viewed as suspicious, it was greeted with outrage by student demonstrators. As Levine mentions, students on campuses across the country viewed computers as large, bureaucratic, war-making machines that supported the military-industrial complex.

Levine makes a big deal of the Cambridge Project, but is there really a concrete connection between surveillance, counterinsurgency, computer networking, and this research effort? If there is, he doesn’t present it in the book. Instead, he relies heavily on an article in the Harvard Crimson by a student activist. He doesn’t even directly quote from the project proposal itself, which should contain at least one or two damning lines. Instead, he lists types of “data banks” the project would build, including ones on youth movements, minority integration in multicultural societies, and public opinion polls, among others. The project ran for five years but Levine never tells us what it was actually used for.

It’s worth pointing out that the DoD was the only organization that was funding computing research in a manner that could lead to real breakthroughs. Licklider and others needed to present military justification for their work, no matter how thin. In addition, as the 1960s came to a close, Congress was tightening its purse strings, which was another reason to trump up their relevance. It’s odd that an investigative reporter like Levine, ever suspicious of the standard line, should take the claims of these proposals at face value.

I spoke with John Klensin, a member of the Cambridge Project steering committee who was involved from the beginning. He has no memory of such data banks. “There was never any central archive or effort to build one,” he told me. He worked closely with Licklider and other key members of the project, and he distinctly recalls the tense atmosphere on campuses at the time, even down to the smell of tear gas. Oddly enough, he says some people worked for him by day and protested the project by night, believing that others elsewhere must be doing unethical work. According to Klensin, the Cambridge Project conducted “zero classified research.” It produced general purpose software and published its reports publicly. Some of them are available online, but Levine doesn’t cite them at all. An ARPA commissioned study of its own funding history even concluded that, while the project had been a “technical success” whose systems were “applicable to a wide variety of disciplines,” behavioral scientists hadn’t benefited much from it. Until Levine or someone else can produce documents demonstrating that the project was designed for, or even used in, counterinsurgency or surveillance efforts, we’ll have to take Klensin at his word.

As for the ARPANET, Levine only provides one source of evidence for his claim that, from its earliest days, the experimental computer network was involved in some kind of surveillance activity. He has dug up an NBC News report from the 1970s that describes how intelligence gathered in previous years (as part of an effort to create dossiers of domestic protestors) had been transferred across a new network of computer systems within the Department of Defense.

This report was read into the Congressional record during joint hearings on Surveillance Technology in 1975. But what’s clear from the subsequent testimony of Assistant Deputy Secretary of Defense David Cooke, the NBC reporter had likely confused several computer systems and networks across various government agencies. The story’s lone named source claims to have seen the data structure used for the files when they arrived at MIT. It is indeed an interesting account, but it remains unclear what was transferred, across which system, and what he saw. This incident hardly shows “how military and intelligence agencies used the network technology to spy on Americans in the first version of the Internet,” as Levine claims.

The ARPANET was not a classified system — anyone with an appropriately funded research project could use it. “ARPANET was a general purpose communication network. It is a distortion to conflate this communication system’s development with the various projects that made use of its facilities,” Vint Cerf, creator of the internet protocol, told me. Cerf concedes, however, that a “secured capability” was created early on, “presumably used to communicate classified information across the network.” That should not be surprising, as the government ran the project. But Levine’s evidence merely shows that surveillance information gathered elsewhere might have been transferred across the network. Does that count as having surveillance “baked in,” as he says, to the early internet?

Levine’s early history suffers most from viewing ARPA or even the military as a single monolithic entity. In the absence of hard evidence, he employs a jackhammer of willful insinuations as described above, pounding toward a questionable conclusion. Others have noted this tendency. He disingenuously writes that, four years ago, a review of Julian Assange’s book in this very publication accused him of being funded by the CIA, when in fact its author had merely suggested that Levine was prone to conspiracy theories. It’s a shame because today’s internet is undoubtedly a surveillance platform, both for governments and the companies whose cash crop is our collective mind. To suggest this was always the case means ignoring the effects of the hysterical national response to 9/11, which granted unprecedented funding and power to private intelligence contractors. Such dependence on private companies was itself part of a broader free market turn in national politics from the 1970s onward, which tightened funds for basic research in computing and other technical fields — and cemented the idea that private companies, rather than government-funded research, would take charge of inventing the future. Today’s comparatively incremental technical progress is the result. In The Utopia of Rules (2015), anthropologist David Graeber describes this phenomenon as a turn away from investment in technologies promoting “the possibility of alternative futures” to investment in those that “furthered labor discipline and social control.” As a result, instead of mind-enhancing devices that might have the same sort of effect as, say, mass literacy, we have a precarious gig economy and a convenience-addled relationship with reality.

Levine recognizes a tinge of this in his account of the rise of Google, the first large tech company to build a business model for profiting from user data. “Something in technology pushed other companies in the same direction. It happened just about everywhere,” he writes, though he doesn’t say what the “something” is. But the lesson to remember from history is that companies on their own are incapable of big inventions like personal computing or the internet. The quarterly pressure for earnings and “innovations” leads them toward unimaginative profit-driven developments, some of them harmful.

This is why Levine’s unsupported suspicion of government-funded computing research, regardless of the context, is counterproductive. The lessons of ARPA prove inconvenient for mythologizing Silicon Valley. They show a simple truth: in order to achieve serious invention and progress — in computers or any other advanced technology — you have to pay intelligent people to screw around with minimal interference, accept that most ideas won’t pan out, and extend this play period to longer stretches of time than the pressures of corporate finance allow. As science historian Mitchell Waldrop once wrote, the polio vaccine might never have existed otherwise; it was “discovered only after years of failure, frustration, and blind alleys, none of which could have been justified by cost/benefit analysis.” Left to corporate interests, the world would instead “have gotten the best iron lungs you ever saw.”

Computing for the benefit of the public is a more important concept now than ever. In fact, Levine agrees, writing, “The more we understand and democratize the Internet, the more we can deploy its power in the service of democratic and humanistic values.” Power in the computing world is wildly unbalanced — each of us mediated by and dependent on, indeed addicted to, invasive systems whose functionality we barely understand. Silicon Valley only exacerbates this imbalance, in the same manner, that oil companies exacerbate climate change or financialization of the economy exacerbates inequality. Today’s technology is flashy, sexy, and downright irresistible. But, while we need a cure for the ills of late-stage capitalism, our gadgets are merely “the best iron lungs you ever saw.”

 Source: This article was published lareviewofbooks.org By Eric Gade

Published in Online Research

For scholars, the scale of Facebook’s 2.2 billion users provides an irresistible way to investigate how human nature may play out on, and be shaped by, the social network.

The professor was incredulous. David Craig had been studying the rise of entertainment on social media for several years when a Facebook Inc. employee he didn’t know emailed him last December, asking about his research. “I thought I was being pumped,” Craig said. The company flew him to Menlo Park and offered him $25,000 to fund his ongoing projects, with no obligation to do anything in return. This was definitely not normal, but after checking with his school, University of Southern California, Craig took the gift. “Hell, yes, it was generous to get an out-of-the-blue offer to support our work, with no strings,” he said. “It’s not all so black and white that they are villains.”

Other academics got these gifts, too. One, who said she had $25,000 deposited in her research account recently without signing a single document, spoke to a reporter hoping maybe the journalist could help explain it. Another professor said one of his former students got an unsolicited monetary offer from Facebook, and he had to assure the recipient it wasn’t a scam. The professor surmised that Facebook uses the gifts as a low-cost way to build connections that could lead to closer collaboration later. He also thinks Facebook “happily lives in the ambiguity” of the unusual arrangement. If researchers truly understood that the funding has no strings, “people would feel less obligated to interact with them,” he said.

The free gifts are just one of the little-known and complicated ways Facebook works with academic researchers. For scholars, the scale of Facebook’s 2.2 billion users provides an irresistible way to investigate how human nature may play out on, and be shaped by, the social network. For Facebook, the motivations to work with outside academics are far thornier, and it’s Facebook that decides who gets access to its data to examine its impact on society.“Just from a business standpoint, people won’t want to be on Facebook if Facebook is not positive for them in their lives,” said Rob Sherman, Facebook’s deputy chief privacy officer. “We also have a broader responsibility to make sure that we’re having the right impact on society.”

The company’s long been conflicted about how to work with social scientists, and now runs several programs, each reflecting the contorted relationship Facebook has with external scrutiny. The collaborations have become even more complicated in the aftermath of the Cambridge Analytica scandal, which was set off by revelations that a professor who once collaborated with Facebook’s in-house researchers used data collected separately to influence elections. ALSO READ: Facebook admits it tracks your mouse movements

“Historically the focus of our research has been on product development, on doing things that help us understand how people are using Facebook and build improvements to Facebook,” Sherman said. Facebook’s heard more from academics and non-profits recently who say “because of the expertise that we have, and the data that Facebook stores, we have an opportunity to contribute to generalizable knowledge and to answer some of these broader social questions,” he said. “So you’ve seen us begin to invest more heavily in social science research and in answering some of these questions.”

Facebook has a corporate culture that reveres research. The company builds its product based on internal data on user behaviour, surveys and focus groups. More than a hundred Ph.D.-level researchers work on Facebook’s in-house core data science team, and employees say the information that points to growth has had more of an impact on the company’s direction than Chief Executive Officer Mark Zuckerberg’s ideas.

Facebook is far more hesitant to work with outsiders; it risks unflattering findings, leaks of proprietary information, and privacy breaches. But Facebook likes it when external research proves that Facebook is great. And in the fierce talent wars of Silicon Valley, working with professors can make it easier to recruit their students.

It can also improve the bottom line. In 2016, when Facebook changed the “like” button into a set of emojis that better-captured user expression—and feelings for advertisers— it did so with the help of Dacher Keltner, a psychology professor at the University of California, Berkeley, who’s an expert in compassion and emotions. Keltner’s Greater Good Science Center continues to work closely with the company. And this January, Facebook made research the centerpiece of a major change to its news feed algorithm. In studies published with academics at several universities, Facebook found that people who used social media actively—commenting on friends’ posts, setting up events—were likely to see a positive impact on mental health, while those who used it passively may feel depressed. In reaction, Facebook declared it would spend more time encouraging “meaningful interaction.” Of course, the more people engage with Facebook, the more data it collects for advertisers.

The company has stopped short of pursuing deeper research on the potentially negative fallout of its power. According to its public database of published research, Facebook’s written more than 180 public papers about artificial intelligence but just one study about elections, based on an experiment Facebook ran on 61 million users to mobilize voters in the Congressional midterms back in 2010. Facebook’s Sherman said, “We’ve certainly been doing a lot of work over the past couple of months, particularly to expand the areas where we’re looking.”

Facebook’s first peer-reviewed papers with outside scholars were published in 2009, and almost a decade into producing academic work, it still wavers over how to structure the arrangements. It’s given out the smaller unrestricted gifts. But those gifts don’t come with access to Facebook’s data, at least initially. The company is more restrictive about who can mine or survey its users. It looks for research projects that dovetail with its business goals.

Some academics cycle through one-year fellowships while pursuing doctorate degrees, and others get paid for consulting projects, which never get published.

When Facebook does provide data to researchers, it retains the right to veto or edit the paper before publication. None of the professors Bloomberg spoke with knew of cases when Facebook prohibited a publication, though many said the arrangement inevitably leads academics to propose investigations less likely to be challenged. “Researchers focus on things that don’t create a moral hazard,” said Dean Eckles, a former Facebook data scientist now at the MIT Sloan School of Management. Without a guaranteed right to publish, Eckles said, researchers inevitably shy away from potentially critical work. That means some of the most burning societal questions may go unprobed.

Facebook also almost always pairs outsiders with in-house researchers. This ensures scholars have a partner who’s intimately familiar with Facebook’s vast data, but some who’ve worked with Facebook say this also creates a selection bias about what gets studied. “Stuff still comes out, but only the immensely positive, happy stories—the goody-goody research that they could show off,” said one social scientist who worked as a researcher at Facebook. For example, he pointed out that the company’s published widely on issues related to well-being, or what makes people feel good and fulfilled, which is positive for Facebook’s public image and product. “The question is: ‘What’s not coming out?,’” he said.

Facebook argues its body of work on well-being does have broad importance. “Because we are a social product that has large distribution within society, it is both about societal issues as well as the product,” said David Ginsberg, Facebook’s director of research.Other social networks have smaller research ambitions, but have tried more open approaches. This spring, Twitter Inc. asked for proposals to measure the health of conversations on its platform, and Microsoft Corp.’s LinkedIn is running a multi-year programme to have researchers use its data to understand how to improve the economic opportunities of workers. Facebook has issued public calls for technical research, but until the past few months, hasn’t done so for social sciences. Yet it has solicited in that area, albeit quietly: Last summer, one scholarly association begged discretion when sharing information on a Facebook pilot project to study tech’s impact in developing economies. Its email read, “Facebook is not widely publicizing the program.”

In 2014, the prestigious Proceedings of the National Academy of Sciences published a massive study, co-authored by two Facebook researchers and an outside academic, that found emotions were “contagious” online, that people who saw sad posts were more likely to make sad posts. The catch: the results came from an experiment run on 689,003 Facebook users, where researchers secretly tweaked the algorithm of Facebook’s news feed to show some cheerier content than others. People were angry, protesting that they didn’t give Facebook permission to manipulate their emotions.

The company first said people allowed such studies by agreeing to its terms of service, and then eventually apologized. While the academic journal didn’t retract the paper, it issued an “Editorial Expression of Concern.”

To get federal research funding, universities must run testing on humans through what’s known as an institutional review board, which includes at least one outside expert, approves the ethics of the study and ensures subjects provide informed consent. Companies don’t have to run research through IRBs. The emotional-contagion study fell through the cracks.

The outcry profoundly changed Facebook’s research operations, creating a review process that was more formal and cautious. It set up a pseudo-IRB of its own, which doesn’t include an outside expert but does have policy and PR staff. Facebook also created a new public database of its published research, which lists more than 470 papers. But that database now has a notable omission—a December 2015 paper two Facebook employees co-wrote with Aleksandr Kogan, the professor at the heart of the Cambridge Analytica scandal. Facebook said it believes the study was inadvertently never posted and is working to ensure other papers aren’t left off in the future.

In March, Gary King, a Harvard University political science professor, met with some Facebook executives about trying to get the company to share more data with academics. It wasn’t the first time he’d made his case, but he left the meeting with no commitment.

A few days later, the Cambridge Analytica scandal broke, and soon Facebook was on the phone with King. Maybe it was time to cooperate, at least to understand what happens in elections. Since then, King and a Stanford University law professor have developed a complicated new structure to give more researchers access to Facebook’s data on the elections and let scholars publish whatever they find. The resulting structure is baroque, involving a new “commission” of scholars Facebook will help pick, an outside academic council that will award research projects, and seven independent U.S. foundations to fund the work. “Negotiating this was kind of like the Arab-Israel peace treaty, but with a lot more partners,” King said.

The new effort, which has yet to propose its first research project, is the most open approach Facebook’s taken yet. “We hope that will be a model that replicates not just within Facebook but across the industry,” Facebook’s Ginsberg said. “It’s a way to make data available for social science research in a way that means that it’s both independent and maintains privacy.” But the new approach will also face an uphill battle to prove its credibility. The new Facebook research project came together under the company’s public relations and policy team, not its research group of PhDs trained in ethics and research design. More than 200 scholars from the Association of Internet Researchers, a global group of interdisciplinary academics, have signed a letter saying the effort is too limited in the questions it’s asking, and also that it risks replicating what sociologists call the “Matthew effect,” where only scholars from elite universities—like Harvard and Stanford—get an inside track.

“Facebook’s new initiative is set up in such a way that it will select projects that address known problems in an area known to be problematic,” the academics wrote. The research effort, the letter said, also won’t let the world—or Facebook, for that matter—get ahead of the next big problem.

Source: This article was published hindustantimes.com By Karen Weise and Sarah Frier

Published in Social

Researchers found that since the prescription opioid crackdown began, dark web sales for the targeted medications have steadily increased.

Rules meant to crack down on the use of opioids have instead turned some individuals to the black market, a new study has found.

UPI reports that in 2014, the U.S. Drug Enforcement Administration (DEA) put new regulations on hydrocodone (e.g. Vicodin), making it more difficult to prescribe and taking away automatic refill options.

From mid-2013 to mid-2015, the number of prescriptions decreased greatly. 

However, some individuals had found another way to access the medications: the internet. Research published in the journal BMJ revealed that since the new regulations were put in place, more people are buying opioids online without a prescription, using “software-encrypted online portals that permit illegal sales and elude regulators.”

Researchers found that in the four years since 2014, opioid sales on the dark web have increased by about 4% annually. 

"This [DEA] action did have the hoped-for effect of reducing the number of prescriptions issued for these products," study author Judith Aldridge, a professor of criminology at the University of Manchester in England, told UPI. "[But] our team found that sales on the so-called 'dark net' of opioid prescription medications increased following the DEA's initiative.”

Aldridge also says it was beyond the one type of medication. 

"And this increase was not just observed for medications containing hydrocodone,” she said. “We also saw increased dark-net sales for products containing much stronger opioids, like oxycodone (OxyContin) and fentanyl.”

A team of investigators used “web crawler” software to look in-depth at 31 "cryptomarkets" that operated before and after the new regulations. In doing so, they found minimal changes to the sales of sedatives, steroids, stimulants or illegal opioids (ones that are not prescribed by medical professionals).

On the other hand, investigators found that dark web sales of prescription opioids had increased in overall sales in 2016, making up about 14% of the sales. They also found that of those, more purchases were made for fentanyl than hydrocodone. In 2014, fentanyl had been the least popular dark web prescription opioid, but in 2016 it was the second most popular.

According to researchers, one difficulty with dark web sales is that they are more complicated to monitor. 

"Solutions here are not simple," Aldridge said. "However, we know very well that our results were entirely predictable. Solutions must combine cutting supply and tackling demand at the same time. This requires making prevention and treatment grounded in good science available for all."

Source: This article was published thefix.com By Beth Leipholtz

Published in Deep Web

Ben-Gurion University of the Negev and University of Washington researchers have developed a new generic method to detect fake accounts on most types of social networks, including Facebook and Twitter.

According to their new study in Social Network Analysis and Mining, the new method is based on the assumption that fake accounts tend to establish improbable links to other users in the networks.

“With recent disturbing news about failures to safeguard user privacy, and targeted use of social media by Russia to influence elections, rooting out fake users has never been of greater importance,” explains Dima Kagan, lead researcher and a researcher in the BGU Department of Software and Information Systems Engineering.

“We tested our algorithm on simulated and real-world datasets on 10 different social networks and it performed well on both.”

The algorithm consists of two main iterations based on machine-learning algorithms. The first constructs a link prediction classifier that can estimate, with high accuracy, the probability of a link existing between two users.

The second iteration generates a new set of meta-features based on the features created by the link prediction classifier. Lastly, the researchers used these meta-features and constructed a generic classifier that can detect fake profiles in a variety of online social networks.

Here’s a helpful video explanation of how it all works:

“Overall, the results demonstrated that in a real-life friendship scenario we can detect people who have the strongest friendship ties as well as malicious users, even on Twitter,” the researchers say. “Our method outperforms other anomaly detection methods and we believe that it has considerable potential for a wide range of applications particularly in the cyber-security arena.”

Other researchers who contributed are Dr. Michael Fire of the University of Washington (former Ben-Gurion U. doctoral student) and Prof. Yuval Elovici, director of Cyber@BGU and a member of the BGU Department of Software and Information Systems Engineering.

The Ben-Gurion University researchers previously developed the Social Privacy Protector (SPP) Facebook app to help users evaluate their friend's list in seconds to identify which have few or no mutual links and might be “fake” profiles.

Source: This article was published helpnetsecurity.com

Published in Social

Ever wondered how the results of some popular keyword research tools stack up against the information Google Search Console provides? This article looks at comparing data from Google Search Console (GSC) search analytics against notable keyword research tools and what you can extract from Google.

As a bonus, you can get related searches and people also search data results from Google search results by using the code at the end of this article.

This article is not meant to be a scientific analysis, as it only includes data from seven websites. To be sure, we were gathering somewhat comprehensive data: we selected websites from the US and the UK plus different verticals.

Procedure

1. Started by defining industries with respect to various website verticals

We used SimilarWeb’s top categories to define the groupings and selected the following categories:

  • Arts and entertainment.
  • Autos and vehicles.
  • Business and industry.
  • Home and garden.
  • Recreation and hobbies.
  • Shopping.
  • Reference.

We pulled anonymized data from a sample of our websites and were able to obtain unseen data from search engine optimization specialists (SEOs) Aaron Dicks and Daniel Dzhenev. Since this initial exploratory analysis involved quantitative and qualitative components, we wanted to spend time understanding the process and nuance rather than making the concessions required in scaling up an analysis. We do think this analysis can lead to a rough methodology for in-house SEOs to make a more informed decision on which tool may better fit their respective vertical.

2. Acquired GSC data from websites in each niche

Data was acquired from Google Search Console by programming and using a Jupyter notebook.

Jupyter notebooks are an open-source web application that allows you to create and share documents that contain live code, equations, visualizations and narrative text to extract website-level data from the Search Analytics API daily, providing much greater granularity than is currently available in Google’s web interface.

3. Gathered ranking keywords of a single internal page for each website

Since home pages tend to gather many keywords that may or may not be topically relevant to the actual content of the page, we selected an established and performing internal page so the rankings are more likely to be relevant to the content of the page. This is also more realistic since users tend to do keyword research in the context of specific content ideas.

The image above is an example of the home page ranking for a variety of queries related to the business but not directly related to the content and intent of the page.

We removed brand terms and restricted the Google Search Console queries to first-page results.

Finally, we selected ahead term for each page. The phrase “head term” is generally used to denote a popular keyword with high search volume. We chose terms with relatively high search volume, though not the absolute highest search volume. Of the queries with the most impressions, we selected the one that best represented the page.

4. Did keyword research in various keyword tools and looked for the head term

We then used the head term selected in the previous step to perform keyword research in three major tools: Ahrefs, Moz, and SEMrush.

The “search suggestions” or “related searches” options were used, and all queries returned were kept, regardless of whether or not the tool specified a metric of how related the suggestions were to the head term.

Below we listed the number of results from each tool. In addition, we extracted the “people also search for” and “related searches” from Google searches for each head term (respective to country) and added the number of results to give a baseline of what Google gives for free.

**This result returned more than 5,000 results! It was truncated to 1,001, which is the max workable and sorted by descending volume.

We compiled the average number of keywords returned per tool:

5.  Processed the data

We then processed the queries for each source and website by using some language processing techniques to transform the words into their root forms (e.g., “running” to “run”), removed common words such as  “a,” “the” and “and,” expanded contractions and then sorted the words.

For example, this process would transform “SEO agencies in Raleigh” to “agency Raleigh SEO.”  This generally keeps the important words and puts them in order so that we can compare and remove similar queries.

We then created a percentage by dividing the number of unique terms by the total number of terms returned by the tool. This should tell us how much redundancy there are in the tools.

Unfortunately, it does not account for misspellings, which can also be problematic in keyword research tools because they add extra cruft (unnecessary, unwanted queries) to the results. Many years ago, it was possible to target common misspellings of terms on website pages. Today, search engines do a really good job of understanding what you typed, even if it’s misspelled.

In the table below, SEMrush had the highest percentage of unique queries in their search suggestions.

This is important because, if 1,000 keywords are only 70 percent unique, that means 300 keywords basically have no unique value for the task you are performing.

Next, we wanted to see how well the various tools found queries used to find these performing pages. We took the previously unique, normalized query phrases and looked at the percentage of GSC queries the tools had in their results.

In the chart below, note the average GSC coverage for each tool and that Moz is higher here, most likely because it returned 1,000 results for most head terms. All tools performed better than related queries scraped from Google (Use the code at the end of the article to do the same).

Getting into the vector space

After performing the previous analysis, we decided to convert the normalized query phrases into vector space to visually explore the variations in various tools.

Assigning to vector space uses something called pre-trained word vectors that are reduced in dimensionality (x and y coordinates) using a Python library called t-distributed Stochastic Neighbor Embedding (TSNE). Don’t worry if you are unfamiliar with this; generally, word vectors are words converted into numbers in such a way that the numbers represent the inherent semantics of the keywords.

Converting the words to numbers helps us process, analyze and plot the words. When the semantic values are plotted on a coordinate plane, we get a clear understanding of how the various keywords are related. Points grouped together will be more semantically related, while points distant from one another will be less related.

Shopping

This is an example where Moz returns 1,000 results, yet the search volume and searcher keyword variations are very low.  This is likely caused by Moz semantically matching particular words instead of trying to match more to the meaning of the phrase. We asked Moz’s Russ Jones to better understand how Moz finds related phrases:

“Moz uses many different methods to find related terms. We use one algorithm that finds keywords with similar pages ranking for them, we use another ML algorithm that breaks up the phrase into constituent words and finds combinations of related words producing related phrases, etc. Each of these can be useful for different purposes, depending on whether you want very close or tangential topics. Are you looking to improve your rankings for a keyword or find sufficiently distinct keywords to write about that are still related? The results returned by Moz Explorer is our attempt to strike that balance.”

Moz does include a nice relevancy measure, as well as a filter for fine-tuning the keyword matches. For this analysis, we just used the default settings:

In the image below, the plot of the queries shows what is returned by each keyword vendor converted into the coordinate plane. The position and groupings impart some understanding of how keywords are related.

In this example, Moz (orange) produces a significant volume of various keywords, while other tools picked far fewer (Ahrefs in green) but more related to the initial topic:

Autos and vehicles

This is a fun one. You can see that Moz and Ahrefs had pretty good coverage of this high-volume term. Moz won by matching 34 percent of the actual terms from Google Search Console. Moz had double the number of results (almost by default) that Ahrefs had.

SEMrush lagged here with 35 queries for a topic with a broad amount of useful variety.

The larger gray points represent more “ground truth” queries from Google Search Console. Other colors are the various tools used. Gray points with no overlaid color are queries that various tools did not match.

Internet and telecom

This plot is interesting in that SEMrush jumped to nearly 5,000 results, from the 50-200 range in other results. You can also see (toward the bottom) that there were many terms outside of what this page tended to rank for or that were superfluous to what would be needed to understand user queries for a new page:

Most tools grouped somewhat close to the head term, while you can see that SEMrush (in purplish-pink) produced a large number of potentially more unrelated points, even though Google People Also Search were found in certain groupings.

General merchandise   

Here is an example of a keyword tool finding an interesting grouping of terms (groupings indicated by black circles) that the page currently doesn’t rank for. In reviewing the data, we found the grouping to the right makes sense for this page:

The two black circles help to visualize the ability to find groupings of related queries when plotting the text in this manner.

Analysis

Search engine optimization specialists with experience in keyword research know there is no one tool to rule them all.  Depending on the data you need, you may need to consult a few tools to get what you are after.

Below are my general impressions from each tool after reviewing, qualitatively:

  • The query data and numbers from our analysis of the uniqueness of results.
  • The likelihood of finding terms that real users use to find performing pages.

Moz     

Moz seems to have impressive numbers in terms of raw results, but we found that the overall quality and relevance of results was lacking in several cases.

Even when playing with the relevancy scores, it quickly went off on tangents, providing queries that were in no way related to my head term (see Moz suggestions for “Nacho Libre” in the image above).

With that said, Moz is very useful due to its comprehensive coverage, especially for SEOs working in smaller or newer verticals. In many cases, it is exceedingly difficult to find keywords for newer trending topics, so more keywords are definitely better here.

An average of 64 percent coverage for real user data from GSC for selected domains was very impressive  This also tells you that while Moz’s results can tend to go down rabbit holes, they tend to get a lot right as well. They have traded off a loss of fidelity for comprehensiveness.

Ahrefs

Ahrefs was my favorite in terms of quality due to their nice marriage of comprehensive results with the minimal amount of clearly unrelated queries.

It had the lowest number of average reported keyword results per vendor, but this is actually misleading due to the large outlier from SEMrush. Across the various searches, it tended to return a nice array of terms without a lot of clutter to wade through.

Most impressive to me was a specific type of niche grill that shared a name with a popular location. The results from Ahrefs stayed right on point, while SEMrush returned nothing, and Moz went off on tangents with many keywords related to the popular location.

A representative of Ahrefs clarified with me that their tool “search suggestions” uses data from Google Autosuggest.  They currently do not have a true recommendation engine the way Moz does. Using “Also ranks for” and “Having same terms” data from Ahrefs would put them more on par with the number of keywords returned by other tools.

 SEMrush   

SEMrush overall offered great quality, with 90 percent of the keywords being unique It was also on par with Ahrefs in terms of matching queries from GSC.

It was, however, the most inconsistent in terms of the number of results returned. It yielded 1,000+ keywords (actually 5,000) for Internet and Telecom > Telecommunications yet only covered 22 percent of the queries in GSC. For another result, it was the only one not to return related keywords. This is a very small dataset, so there is clearly an argument that these were anomalies.

Google: People Also Search For/Related Searches 

These results were extremely interesting because they tended to more closely match the types of searches users would make while in a particular buying state, as opposed to those specifically related to a particular phrase. 

For example, looking up “[term] shower curtains” returned “[term] toilet seats.”

These are unrelated from a semantic standpoint, but they are both relevant for someone redoing their bathroom, suggesting the similarities are based on user intent and not necessarily the keywords themselves.

Also, since data from “people also search” are tied to the individual results in Google search engine result pages (SERPs), it is hard to say whether the terms are related to the search query or operate more like site links, which are more relevant to the individual page.

Code used

When entered into the Javascript Console of Google Chrome on a Google search results page, the following will output the “People also search for” and “Related searches” data in the page, if they exist.

1    var data = {};
2    var out = [];
3    data.relatedsearches = [].map.call(document.querySelectorAll(".brs_col p"), e => ({ query: e.textContent }));
4    
5    data.peoplesearchfor = [].map.call(document.querySelectorAll(".rc > div:nth-child(3) > div > div > div:not([class])"), e => {
6    if (e && !e.className) {
7    return { query: e.textContent };
8     }
9     });
10   
11    for (d in data){
12
13    for (i in data[d]){
14    out.push(data[d][i]['query'])
15     }
16
17    }
18    console.log(out.join('\n'))

In addition, there is a Chrome add-on called Keywords Everywhere which will expose these terms in search results, as shown in several SERP screenshots throughout the article. 

Conclusion

Especially for in-house marketers, it is important to understand which tools tend to have data most aligned to your vertical. In this analysis, we showed some benefits and drawbacks of a few popular tools across a small sample of topics. We hoped to provide an approach that could form the underpinnings of your own analysis or for further improvement and to give SEOs a more practical way of choosing a research tool.

Keyword research tools are constantly evolving and adding newly found queries through the use of clickstream data and other data sources. The utility in these tools rests squarely on their ability to help us understand more succinctly how to better position our content to fit real user interest and not on the raw number of keywords returned. Don’t just use what has always been used. Test various tools and gauge their usefulness for yourself.

 Source: This article was published searchengineland.com By R Oakes

Published in Online Research

Our editors delve into Curiosity's top stories every day on a podcast that's shorter than your commute. Click here to listen and learn — in just a few minutes!

The internet contains at least 4.5 billion websites that have been indexed by search engines, according to one Dutch researcher. That huge number barely scratches the surface of what's really out there, however. The rest is known as the deep web, which is 400 to 500 times larger than the surface internet, according to some estimates.

What Makes The Deep Web ... Deep?

It's not deep like sad, non-rhyming poetry, nor is it deep like the unexplored depths of the ocean. The deep web is actually so accessible that you use it every time you check your email. What sets it apart is that its sites can't be reached via search engine; the "let me Google that for you" meme, delightful as it is, doesn't apply. You need to know the URL or have access permissions to view a deep-web site.

The deep web is about as mundane as the surface web, really — it's just wrapped in a thin layer of secrecy. Mostly, it's emails, social media profiles, subscription sites like Netflix, and anything you need to fill out a form to access. But because the deep web is hidden from search engines, some people use it for more nefarious purposes.

Welcome to the Dark Web

The dark web and the deep web aren't synonymous. The dark web is a sliver of the deep web made up of encrypted sites. Here, near-total anonymity reigns. Encrypted sites lack the DNS and IP addresses that usually make websites identifiable. More confusing still: To access them, users have to use encrypting software that masks their IP addresses, making the users hard to identify, too.

Unsurprisingly, many dark-web sites specialize in illegal goods and services. The now-defunct Silk Road, for instance, was an online drug store — and not in the CVS sense. When its creator, Ross Ulbricht, was arrested in 2013, Silk Road had 12,000 listings for everything from weed to heroin. (Ulbricht was sentenced to life in prison.) The dark web also provides shady resources for hitmen, terrorists, and other criminals; overall, its illicit marketplaces generate more than $500,000 per day. Just accessing the dark web can set off red flags at the FBI.

This is ironic, since Tor, the most popular software for making and accessing dark websites, was originally created by the U.S. Navy. Even today, Tor is funded by the U.S. government. Washington isn't secretly supporting the online heroin trade, though — there are actually plenty of other, less shady uses for Tor's encrypting services. When activists speak out against authoritarian regimes, for instance, Tor can help them protect their privacy; the same goes for whistleblowers, and Average Joes spooked by Facebook's forthcoming eye-tracking feature. Never forget: Tor can also get you into ... dark web bookclubs? If you're into that.

 Source: This article was published curiosity.com

Published in Deep Web

Just contemplating the need to do market research for your business can feel overwhelming. However, you need to jump in because market research is key to the success of your business. First, you need to understand the difference between "market research" and "marketing research."

Market research is when you have narrowed down a specific "target, " and you are delving into the behavior of that target.

In other words, its research into a very narrow group of consumers.

Marketing research entails dealing with a broad range of consumers. Marketing research includes "market" research, but it also delves into more. The best way to differentiate the two is to understand that marketing research is essentially about researching the marketing "process" of a company—not just "who" they are targeting.

First, let's look at how to implement marketing research, which includes the following steps and the questions you should be asking yourself along the way:

  1. Problem definition. The problem is the focus of your research. For example, "why are sales soaring in your Midwest territory, but dismal in other parts of the country?
  2. Data collection method and needs. This is where you ask, "how will I collect the data I need to solve the problem? Do I use surveys, telephone calls or focus groups?
  3. Determine sample method. Sampling represents those you will be collecting information from. You need to ask yourself, "what sampling method will I use? Will it be a random sampling, a sampling that contains a similar element, or a natural sampling?
  4. Data analysis. You need to figure out how you will you analyze the data. Will you use software or do it by hand? Also, how accurately do the results need to be?
  5. Determine budget and timeframe. You must determine how much you're willing to spend on the research and how soon it needs to be completed.
  6. Analysis of the data. At this point, you conduct the analysis of the data that has been collected in the previous Steps.
  7. Error check. Be sure to check for errors in the data you've collected and analyzed. Errors can occur in the sampling method and data collections.
  8. Create your report. The final step of marketing research is to draft a report on your findings. Your report should contain tables, charts, and/or diagrams. It's important that your report clearly communicates the results that you found. Your findings should lead to a solution to the problem you identified in Step One.

3 Key Benefits of Market Research

Market research also provides many benefits. It takes the guesswork out of marketing and gives you data that you can use to drive your marketing strategy—and accomplish your objectives and goals. It's a systematic approach that can make your marketing not only easier but more effective. 

Market research includes the following benefits:

  • Communication driver. It drives your communication not only with your current customer base but your target prospects as well. 
  • Identifies opportunity. Market research shows you where the opportunities are and helps you identify not just high-level opportunities but assists in showing you the more immediate "low hanging fruit" opportunities. 
  • Lowers risk. Detailed data keeps you focused on the real opportunities and helps you avoid unproductive areas. 

Source: This article was thebalancesmb.com By LAURA LAKE

Published in Market Research

Whether Conducting Academic Research or Purely Scientific Research, These Sites can be an Invaluable Aid.

Researching is the most crucial step in writing a scientific paper. It is always a well-researched scientific paper that inspires the assessor. At the same time, it must have genuine and authentic information for credibility. With the development in the Internet industry, i.e., web resources, researching for scientific materials has now become a matter of a few clicks. Now students can get information on any topic pertaining to science through academic search engines. They provide a centralized platform and allow the students to acquire literature on any topic within seconds.

scientific academic research image top internet sources

While there are many academic search engines available, there are some that have the most trusted resources. They provide information on a range of topics from Engineering and technology to Biology and Natural Science. They provide a one-stop solution to all research-related needs for a scientific paper. Besides, they provide a personal and customized way to search research materials on any given topic. This article will focus on some popular academic search engines that have revolutionized the way information is researched by the students. They are rich in information and have the highest level of credibility.

  1. Google Scholar (http://scholar.google.com/):Google Scholar is a free academic search engine that indexes academic information from various online web resources. The Google Scholar lists information across an array of academic resources, mostly are peer-reviewed. It works in the same manner as Scirus. Founded in 2004, it is one of the widely used academic resources for researchers and scholars.
  2. CiteSeerx(http://citeseerx.ist.psu.edu): CiteSeerx is a digital library and an online academic journal that offer information within the field of computer science. It indexes academic resources through autonomous citation indexing system. This academic database is particularly helpful for students seeking information on computer and information sciences. It offers many other exclusive features to facilitate the students with the research process that include: ACI – Autonomous Citation Indexing, reference linking, citation statistics, automatic metadata extraction and related documents. Founded in 1998, it is the first online academic database and has since evolved into a more dynamic and user-friendly academic search engine.
  3. GetCITED(http://www.getcited.org/): GetCITED is another powerful tool for searching scientific information. It is an online academic database that indexes academic journals and citations. It is a one-stop platform that offers everything related to academic publications such as chapters, conference papers, reports and presentations. You can even browse through the bibliographies to search related details. Furthermore, you can find information on any author and his published works. The two ‘most outstanding’ features of this academic search engine tool include: ‘a comprehensive database’ and ‘discussion forum’. It allows every member from academia to contribute in its database resources. It has over 3,000,000 written by more than 3,00,000 authors.
  4. Microsoft Academic Research(http://academic.research.microsoft.com/): Microsoft academic research is yet another top search engine for academic resources. Developed by Microsoft Research, it has more than 48 million publications written by over 20 million authors. It indexes range of scientific journals from computer science and engineering to social science and biology. It has brought in many new ways to search academic resources, such as papers, authors, conferences, and journals. This academic search engine allows you to search information based on authors or domains.
  5. Bioline International(http://www.bioline.org.br/): Bioline is among the most trusted and authentic search engines that have peer-reviewed academic journals on public health, food and nutritional security, food and medicine and biodiversity. It provides free access to peer-reviewed journals from third world countries. It promotes an exchange of ideas through academic resources. Founded in 1993, it has 70 journals across 15 countries that offer information on subjects like crop science, biodiversity, public health and international development.
  6. Directory of Open Access Journals(http://www.doaj.org/): Director of Open Access Journals (DOAJ) is yet another free search engine for scientific and scholarly resources. The directory offers a huge range of topics within scientific areas of study. It is among the richest sources of the scholarly database with over 8,000 journals available on different topics. All the journals are thoroughly peer-reviewed.
  7. PLOS ONE (http://www.plosone.org/): Founded in 2006, PLOSE ONE provides a free access platform to everyone searching for science-related information. All the articles published on PLOS ONE are published after going through a strict peer-reviewed process. This academic database has a meticulous procedure for publishing a journal. You can find plenty of articles and academic publications using this platform.
  8. BioOne (http://www.bioone.org/): An excellent search engine for scientific information, BioOne contains academic resources for biological, environmental and ecological sciences. Established in 2000, it started as an NGO and later became an online academic journal directory. The journal gives free access to over 25000 institutions all over the world.
  9. Science and Technology of Advanced Materials(http://iopscience.iop.org/1468-6996/): First published in 2000, the science and technology of advanced materials became online in 2008. This peer-reviewed academic journal offers free access to academic journals on major areas of science and technology. The academic directory is totally free of cost and provides easy and simple access to the plethora of information covering scientific subject-matters.
  10. New Journal of Physics (http://iopscience.iop.org/1367-2630):New Journal of Physics is an online scientific search engine that has academic databases with physics as core subject. Founded in 1998, it is co-founded by the Institute Of Physics and Deutsche Physikalische Gesellschaft. The search engine offers academic journals on diversified topics with physics as central theme.
  11. ScienceDirect(http://www.sciencedirect.com/): “A leading full-text scientific database offering journal articles and book chapters from more than 2,500 journals and almost 20,000 books.”

The above mentioned academic database and directories are among the most trusted search engines for scientific research. They offer information on possibly all the major areas of science including computer and technology, biology, environmental science and social sciences, and other areas of academic research.

Source: This article was emergingedtech.com By Katie Alice

Published in Search Engine

In the age of digital journalism, advanced online search techniques are becoming requisite skills for successful careers in journalism. With hundreds of millions of sites indexed, Google is undoubtedly the most powerful search engine, but it’s easy to miss out on a lot of that power if we don't know the best techniques for asking questions. Although Google will almost always have answers, the goal is to find the relevant ones.

Fortunately, there are a number of search techniques that journalists (and researchers in general) can use to dramatically improve search results. Like everything in life, it requires a bit of tenacity, but it's not hard to learn. This guide is intended to help professional and citizen journalists better understand how Google works. It explains how to use a variety of search operators and techniques to narrow down search results. Let’s get started.

1. Consider Exact Phrases

Looking for a needle in a haystack? One of the most basic techniques in searching Google is to explicitly declare what you’re looking for by entering phrases in quotation marks. This is especially relevant when the phrases have three or more words in them. If you just enter a bunch of words, Google will assume those words could be in any order. But if you put quotation marks around them, then Google knows you're looking for that phrase in the exact word order, and returns results that potentially bring you closer to the right answer.

So, for example, if we are interested in searching for "lagos farmers market," and we're looking for results that exactly match our query, putting the search words in quotes gives us fewer, and invariably more targeted results. In the screenshots below, searching without quotation marks returned 254,000 results, whereas the use of quotation marks reduced the results to 323, eliminating a whopping 253,677 irrelevant results.

2. Do Word Exclusion

Now, 323 results is a lot of improvement from 254,000 results. However, if we examine the results page, the first result – which is in most cases the most relevant – seems to be referring to a Lagos in Portugal. Assuming we are interested in Lagos, Nigeria, we need to find a way of excluding Portugal from our results list. To do this, we simply "minus" Portugal from the returned results by adding "-Portugal" to our search query. From the screenshot below, you can see we are able to get the returned results down by 182, to 141 results. The first result is also now a Yellow Pages link, which is most likely what we’re looking for.

3. Use Site Operators (site:)

Assuming we know for sure that the information we need is on Yellow Pages, we can further narrow our search to the specific site by using a unique operator called site. The site operator allows us to restrict search results to specified sites. In the logos farmers market example, we can reduce the results to two by specifying that Google restricts its search to yellowpages.net.ng.

4. Use Filetype Operators (filetype:)

Sometimes we are more interested in specific file types such as PDF, Word Document, Excel Spreadsheet, etc. Google gives us the power to filter search results to file types by using the filetype keyword. Using the logos farmers market example, we can narrow down to results in PDF as shown below.

Replacing filetype:pdf with filetype:xls returns results in Microsoft Excel formats, and filetype:doc returns results in Microsoft Word.

5. Choose your words, carefully

This is non-technical but very crucial. Understanding the jargon used in the targeted field will lead to better results. For example, search queries like "mortality rate" will likely return more relevant results than "death rate."

This list contains some of the most frequently used advanced search techniques. It was designed to whet your appetite and get you to rethink how you approach searching on Google. It is therefore far from exhaustive. Have a look at Google's own advanced search page and additional resources at googleguide.com by Nancy Blachman and Jerry Peek, two experts who are not affiliated with Google. Once you get your head around these techniques, try a combination of any or all to take the best advantage of this powerful search engine.

Source: This article was published icfj.org By Temi Adeoye

Published in Online Research

Data collection for marketing research is a detailed process where a planned search for all relevant data is made by a researcher. The success of marketing research is contingent on the integrity and relevance of the data. And to a high degree, the quality of the data depends on the methods of data collection used. The selection and use of methods for conducting marketing research require a great deal of experience and expertise in order to correctly gage suitability.

These methods fall into two types of research categories, which are Qualitative Research and Quantitative Research. Qualitative Research is generally used to develop an initial understanding of the problem. It is non-statistical in nature and the answers are derived from the data itself. It is used in exploratory and descriptive research designs. Qualitative data can be procured through a variety of forms like interview transcripts; documents, diaries, and notes made while observing. Quantitative Research, on the other hand, quantifies the data and generalizes the results from the sample to the population.

There are two types of data:

  1. Primary Data – Data that is collected first hand by the researcher. This data is specifically collected for the purpose of the study and addresses the current problem. This is original data that is collected by the researcher first hand.
  2. Secondary Data – Data from other sources that has been already collected and is readily available. This data is less expensive and more quickly attainable from various published sources. Secondary data is extremely useful when primary data cannot be obtained at all.

The challenge lies in the case of method selection for collecting primary data. The method has to be relevant and appropriate. This will be the most important decision prior to beginning market research.

The market research process consists of 6 distinct steps:

  • Step 1 - Determine the research problem and objectives
  • Step 2 - Cultivate the overall research plan
  • Step 3 – Collect the data
  • Step 4 – Analyze the data
  • Step 5 – Present or publish the findings
  • Step 6 – Use the findings to make an informed decision

To further explore Step 3, here a few effective methods of data collection:

1. Telephone Interviews

The biggest advantage of telephone interviews is that is saves cost and time. Today, accessing people via telephone is so much easier because almost everyone has one. Another advantage is fewer interviewers are required in order to conduct telephone interviews than face-to-face interviews.

2. Online Surveys

Given the current myriad of technological developments, the use of online surveys has rapidly increased. It may well be the least expensive way to reach the greatest amount of people – all over the world. Once an online survey has been designed, it can be stored easily, revised and reused as needed from time to time. The key is in the design and layout of the survey so that respondents don’t overlook a survey in their crowded inboxes. The response time is quick so online surveys have become the preferred method of data collection for many consumer satisfaction surveys and product and service feedback. It is easy to track respondents, non-respondents and results through the data collection process. Electronic reminders can be sent easily at a very low cost. Respondents have the option to begin the survey, stop, save the responses at a later more convenient time. Research shows that respondents tend to answer questions more truthfully than when engaged through other methods.

3. Face to Face Interviews

This method is one of the most flexible ways to gather data and gain trust and cooperation from the respondents. Besides that, interviewing respondents in person means their non-verbal language can be observed as well. It is especially useful to detect discomfort when respondents are discussing sensitive issues. Respondents have more time to consider their answers and the interviewer can gain a deeper understanding of the validity of a response. It is also easier to maintain their interest and focus for a longer period. Focus Group Interviewsentail more respondents at one time.

Face to face interviews can also take place via Intercept Interviews as well. These interviews can take place on the spot at shopping malls, street corners or even at the threshold of people’s homes. It is understandable why these types of interviews must be brief, to the point and free of from distasteful questions as there is a strong risk of the potential respondent leaving. These face to face interactions can be time-consuming so enlist a trusted company like Dattel Asia to provide the data needed with unprecedented levels of transparency. Dattel Asia is ASEAN’s leading data collection company that utilizes tablets, digital tools, and artificial machine learning systems for data collection. A reliable face-to-face data collection service provider that has over 250 skilled Field Data Associates and more than 310,000 unique and verified respondents in their data repository.

Source: This article was published bigdata-madesimple.com By Menaka George

Published in Market Research
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