Journalism 2.0: Rumor Monitoring in Social Media
Transcript of Journalism 2.0: Rumor Monitoring in Social Media
Rumor monitoring in Social Media
AEJ 2.0 – November 11th, 2015
Laura Tolosi and Georgi Georgiev
Fake photo showing London Eye burning circulated on Twitter. http://www.theguardian.com/uk/2011/dec/07/how-twitter-spread-rumours-riots
Rumor on imminent banckrupcy of Fibank emerged in Bulgaria in 2014, creating mass panic. http://www.pfhub.com/bulgarian-bank-first-investment-bank-fibank-hit-by-second-bank-run-in-one-week-880/
Мониторинг на слухове в интернет
Project PHEME
• The project PHEME is funded by the 7th
EU Framing Programme, Grant No. 611233 • PHEME coined the term phemes:
− memes are thematic motifs that spread through social media in ways analogous to genetic traits − phemes add truthfulness and deception to the mix − named after ancient Greek Pheme, “embodiment of fame and notoriety, her favor being notability, her wrath being scandalous rumors"
PHEME focuses on a fourth crucial, but hitherto largely unstudied, challenge of big data: veracity. The other three: volume, velocity and variety.
http://en.wikipedia.org/wiki/Pheme
• By journalists: mostly manual
• Technological challenges
− Analysis is post-hoc, scarce information, fast reaction needed
− Some rumors could take days, weeks or even months to die out
− Ill-meaning humans can currently outsmart computers and appear genuine
Rumor analysis
• Create a computational framework for automatic discovery and verification of rumours, at scale and fast
Goal
• Investigate models and algorithms for automatic extraction and verification of four kinds of rumours and their textual expressions (i.e. phemes):
− speculation (e.g. an analyst claiming the Bank of England will raise interest rates at their next meeting), − controversy (e.g. aluminium may or may not cause Alzheimer’s), − misinformation (unverifiable information, e.g. misrepresentation and quoting out of context), and − disinformation (intentionally false, e.g. Obama is a Muslim).
Rumor classification
Definition: “a circulating story of questionable veracity, which is apparently credible but hard to verify, and produces sufficient skepticism and/or anxiety.”
• Capture sequen6al features of conversa6on thread
• Analyze the effect of interac6on at a given point
Conversational aspects of rumors
Zubiaga et al., 2015, Crowdsourcing the Annotation of Rumorous Conversations in Social Media, IW3C2
Rumor stories on Twi@er
• Putin missing, dead, sick or coup? • Ferguson police shooting dead a black teenager • Prince surprise concert in Toronto • Swiss museum to acquire Gurlitt collection, art stolen by nazis • Ottawa shooting and killing of a police officer in front of the Parliament • Sydney hostage siege by lone gunman • Germanwings flight crash • Charlie Hebdo terrorist attack
User profile
• Number of followers • Number of friends • Number of tweets posted
User profile
• Number of followers • Number of friends • Number of tweets posted • few followers • many friends • many tweets posted • … likely rumor!!!
User ID
• Users that posted rumors will do so in the future, too • Tabloid newspapers, famous people … l User with 88% rumors in our data:
Source (URL) cited
Citing a trusted source vs. a questionable one
Text characteris6cs
l Over-capitalization l l Over-use of punctuation “...”, “!”, “?” l Length of the message
Model – overview
Classification tree: F-measure of about 65%
Classifica6on of en6re conversa6ons
• Number of direct replies, total number of replies
• Number of retweets
• Sentiment of replies
• Velocity of replies
• Rumors spread faster than non-rumors
• Certain patterns of replies
• Inquisitive or contradicting answers like: “Really?” “That's not true..” etc.
Selected Clients
Thank you!
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