... A big problem with theories floated on social media is that information can go viral simply because it is popular, whether or not it is true. Patrick Meier of the Qatar Computing Research Institute (QCRI) in Doha is building Verily, a system that allows users to submit verification requests for information they are interested in. Each request prompts a crowd of online workers to set off into their networks to figure it out. The system gathers evidence for and against the claim, though it won't pass judgement.
...By training machine learning algorithms on huge data sets, Meier is building up profiles of the classes of digital evidence that tend to be credible, and those that are not.
As an example, Meier points to a recent study of misinformation on Twitter after the 2010 Chilean earthquake. Carlos Castillo of the QCRI and colleagues showed that non-credible tweets tend to spark responses that question or rebuke them – a trait software can be trained to recognise. "Non-credible information propagates across the twittersphere leaving very specific ripples behind," says Meier. "You could absolutely start having a probability – a percentage chance that particular tweets are not credible."
I am glad to announce the third edition of the Web Quality workshop, to be held on May 13th, 2013 in Rio de Janeiro, Brazil. The workshop is co-located with the World Wide Web conference.
This year's theme is the question: Signal or Noise?. The Web and social media keep on growing and playing an ever increasing role in our lives. In this context, finding relevant, timely and trustworthy content in a sea of seemingly irrelevant chatter remains a challenging research issue.
The workshop will bring together practitioner and researchers working on key problem areas such as modelling trust and author reputation, detecting abuse and spam, finding high-quality content, uncovering plagiarism, among other topics.
Slate.com writes about our upcoming study on Internet Research extending our findings presented in "Information Credibility on Twitter" [pdf].
Social media hoaxes: Could machine-learning algorithms help debunk Twitter rumors before they spread?
...
In a new paper, to be published in the journal Internet Research next month, the authors of the Chile earthquake study—Carlos Castillo, Marcelo Mendoza, and Barbara Poblete—test out their algorithm on fresh data sets and find that it works pretty well. According to Meier, their machine-learning classifier had an AUC, or “area under the curve,” of 0.86. That means that, when presented with a random false tweet and a random true tweet, it would assess the true tweet as more credible 86 percent of the time. (An AUC of 1 is perfect; an AUC of 0.5 is no better than random chance.)
My guess is that a knowledgeable and experienced human Twitter user could do better than that under most circumstances. And of course, if a given algorithm became widespread, committed trolls like the Hurricane Sandy villain @ComfortablySmug could find ways to game it. Still, an algorithm has the potential to work much faster than a human, and as it improves, it could evolve into an invaluable "first opinion" for flagging news items on Twitter that might not be true.
This is your chance to get an inside look at the big challenges Yahoo! research scientists are working on while driving your research forward. Learn more about the real-world problems facing our industry, then focus on and solve these fundamental challenges alongside the top minds in the field.
Yahoo! Labs is inviting PhD students working in each of their core research areas to review these challenges and submit an application between January 20th - March 9th, 2012 to be considered for the Key Scientific Challenges Program.
The workshop will provide the research communities working on web quality, web spam, abuse, credibility, and reputation topics with a survey of current
problems and potential solutions:
Assessing the credibility of content and people on the web and social media
Measuring quality of web content
Uncovering distorted and biased content
Modeling author identity, trust, and reputation
Role of groups and communities
Multimedia content credibility
Fighting spam, abuse, and plagiarism on the Web and social media
Reducing web spam
Reducing abuses of electronic messaging systems
Detecting abuses in internet advertising
Uncovering plagiarism and multiple-identity issues
Full papers are limited to 8 pages, while short papers to 4 pages. Papers should be formatted according to the ACM style guide and submitted via https://www.easychair.org/conferences/?conf=webquality2012 As in previous years, we are making arrangements to include the accepted papers in the ACM Digital Library.
Important dates
Feb 14, 2012: Paper submission deadline
Mar 4, 2012: Notification of acceptance
Mar 20, 2012: Camera ready copy deadline
Apr 16, 2012: Workshop date
Organizers
Adam Jatowt (Kyoto University)
Carlos Castillo (Yahoo! Research)
Zoltan Gyongyi (Google Research)
Katsumi Tanaka (Kyoto University)
Program committee
Ching-man Au Yeung (Astri)
James Caverlee (Texas A&M University)
Matt Cutts (Google)
Brian Davison (Lehigh University)
Dennis Fetterly (Microsoft)
Andrew Flanagin (University of California, Santa Barbara)
Panagiotis Metaxas (Wellesley College)
Miriam Metzger (University of California, Santa Barbara)
... A big problem with theories floated on social media is that information can go viral simply because it is popular, whether or not it is true. Patrick Meier of the Qatar Computing Research Institute (QCRI) in Doha is building Verily, a system that allows users to submit verification requests for information they are interested in. Each request prompts a crowd of online workers to set off into their networks to figure it out. The system gathers evidence for and against the claim, though it won't pass judgement.
Slate.com writes about our upcoming study on Internet Research extending our findings presented in "
The Anti-Social Web: Credibility and Quality Issues on the Web and Social Media 
