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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