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IC2S2 2021 "Modeling the Spread of Fake News on Twitter"

IC2S2 2021 "Modeling the Spread of Fake News on Twitter"

Slides for "Modeling the Spread of Fake News on Twitter" in 7th International Conference on Computational Social Science (IC2S2)

99329e6255a0fe867a64beb897cf3c84?s=128

taichi_murayama

July 10, 2021
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      5IF6OJWFSTJUZPG5PLZP 7th IC2S2 (2021)
  2. 2 l Increasing the number of access to news on

    social media l About two-thirds of American adults get news on social media l News from various doubtful sources has been spread by many users l Occasionally, fake news is spread. l Impact of Fake news l The spread of fake news on social media impacts on various fields; democracy, social media ecosystem, journalism, economics and etc. Background from: Pew Research Center from: https://news.northeastern.edu/2016/11/23/3qs-what-a-shame-how-to-filter-out-fake-news/
  3. 3 Example of fake news: Health • Various fake news

    related to the COVID-19 • Vitamin C cures the COVID-19 • 5G mobile networks spread COVID-19 From: https://k-tai.watch.impress.co.jp/docs/news/1246278.html Background
  4. 4 Research Objective Modeling the cascade of fake news on

    Twitter • Understanding the cascade of fake news could be useful in the application of fake news; detection and mitigation. • Model the re-share probability of fake news leveraging by post time and user profile (number of followers) Background
  5. 5 Research Objective Modeling the cascade of fake news on

    Twitter • Understanding the cascade of fake news could be useful in the application of fake news; detection and mitigation. • Model the re-share probability of fake news leveraging by post time and user profile (number of followers) Breaking News!! Discovery a huge cat Fake News Timeline Post by users 2020/01/01 12:00 The cut is huge! http://.... Modeling Post probability Background
  6. 6 Hypothesis: the cascade of fake news Fake news cascade

    on social media is comprised of two stage cascades • First Cascade has the characteristics as ordinary news story • Second Cascade has the characteristics of correction because users recognize the falsity of the news item around a correction time !" Proposed Model                                                First cascade (the characteristics of ordinary news) Second cascade (the characteristics of correction)
  7. 7 Base Model: Time-Dependent Hawkes Process l Hawkes Process: one

    of point process models whose defining characteristic is that they “self-excite” l Calculate the probability of the next event from past events and elapsed time. From: A Tutorial on Hawkes Processes for Events in Social Media Event timing Probability of event Modeling Proposed Model
  8. 8 Proposed Model Timeline Post on social media Modeling The

    probability of post between t, t + ∆% = λ % ∆% = ((%) ∑ ,:./0. 1, 2(% − %, ) Infection rate How many people remember? Base Model: Time-Dependent Hawkes Process (TiDeH) ((%): infection rate at time t 1, : number of followers at 4.5 post %, : time at 4.5 post 2: the function of how much do you remember
  9. 9 Proposed Model Timeline Post on social media Modeling The

    probability of post between t, t + ∆% = λ % ∆% = ((%) ∑ ,:./0. 1, 2(% − %, ) Infection rate How many people remember? ( % = 4 1 − 6 sin 2; <= % + >? @A./C 4the intensity of information 6: the relative amplitude of the oscillation >? : phase D : the characteristic time of popularity decay ((%): infection rate at time t 1, : number of followers at E.F post %, : time at E.F post 2: the function of how much do you remember Circadian Rhythm the freshness Base Model: Time-Dependent Hawkes Process (TiDeH)
  10. 10         

                                                                                                       Time (hour) ade ) cade ion) s        Modeling        Time (hour) Posting Activity   ! " = $% " ℎ% " + $( (")ℎ( (") "+ Split the cascade at the correction time "+ $% " ℎ% " $( (")ℎ( (") Proposed Model
  11. 11         

                                                                                                       Time (hour) ade ) cade ion) s        Modeling        Time (hour) Posting Activity   ! " = $% " ℎ% " + $( (")ℎ( (") "+ Split the cascade at the correction time "+ $% " ℎ% " $( (")ℎ( (") Proposed Model ℎ% " = , -:/012-3(/, /5) 6- 7(" − "- ) ℎ( " = , -:/5 1 /0 1/ 6- 7(" − "- ) from "+ until "+ 9the intensity of information :: the relative amplitude of the oscillation ;< : phase = : the characteristic time of popularity decay $(") Our hypothesis The first cascade has the characteristics of ordinary news, while second cascade has the characteristics of correction
  12. 12 Dataset • Recent Fake News (RFN) • Collect 7

    fake news, which reported by “Politifact” and “Snopes” from March to May, 2019. • Each news had over 300 posts and kept posting over 36 hours from the initial post. • Fake News in Tohoku earthquake (Tohoku) • Collect 19 fake news related to Tohoku earthquake, which reported by Japanese news media* from March 12th to March 24th 2011. • Each news had over 300 posts and kept posting over 36 hours from the initial post. &WBMVBUJPO * https://blogos.com/article/2530/
  13. 13 &YQFSJNFOU &WBMVBUJPO Task: Predict the number of posts about

    fake news in the future Results: Proposed method achieved higher accuracy than other methods in two dataset. (100% in RFN and 89% in Tohoku) The smaller value is better
  14. 14 Verification of the correction time !" • Check the

    text around the correctio time !" • Verify whether the proposed model can properly estimate the correction time !" from text of posts • Count words that mean hoax or correction (e.g. fake, not true…) • Our hypothesis The first cascade has the characteristics of ordinary news, while second cascade has the characteristics of correction %JTDVTTJPO Blue line indicate the correction time Black line indicates the number of fake words
  15. 15 Verification of the correction time !" : Word cloud

    %JTDVTTJPO Example: Fake news “Turkey Donates 10 Billion Yen to Japan” • Compare the word cloud around the correction time (!" = 37) • Before &', words related to the news “pro-Japanese” appear. • After &', some user point out the news is “false rumor.”
  16. 16 Conclusion • Model posts of fake news on Twitter

    as two cascades comprised of ordinary news and correction of fake news • The proposed model achieves higher accuracy in the prediction task of number of posts. • Around the correction time !" , some user begin to point out the news is “false rumor.”