Machine Leaning & Social Media
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Cover Social media for visually challenged, fake news / mis information, slefie deaths, killfie, telephone scams, gab, code mixed content, stance prediction, hate speech, identity resolution,
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Machine Leaning & Social Media
- 1. ML & Social Media 4th Summer School on Machine Learning July 13, 2019 Ponnurangam Kumaraguru (“PK”) IIIT Delhi TEDx & ACM Distinguished Speaker Linkedin/in/ponguru/ fb/ponnurangam.kumaraguru @ponguru
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- 3. Keywords you got to know in last 120hrs? 3
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- 14. Methodology 14
- 15. Credibility Modeling 15 Feature set Features (45) Tweet meta-data Number of seconds since the tweet; Source of tweet (mobile / web/ etc); Tweet contains geo-coordinates Tweet content (simple) Number of characters; Number of words; Number of URLs; Number of hashtags; Number of unique characters; Presence of stock symbol; Presence of happy smiley; Presence of sad smiley; Tweet contains `via'; Presence of colon symbol Tweet content (linguistic) Presence of swear words; Presence of negative emotion words; Presence of positive emotion words; Presence of pronouns; Mention of self words in tweet (I; my; mine) Tweet author Number of followers; friends; time since the user if on Twitter; etc. Tweet network Number of retweets; Number of mentions; Tweet is a reply; Tweet is a retweet Tweet links WOT score for the URL; Ratio of likes / dislikes for a YouTube video
- 16. Implementation
- 18. Harvard (1839) – Harvard – Harvard – Harvard – MIT – Northwestern – UIUC – WUSL – CMU (2009) – IIITD (2015) 18
- 19. Different methods – #Hashtag level – Tweet level – SVM – Decision tree – Sentiment analysis – Text CNN – LSTM – Autoencoders – Language models – Knowledge Graph – Multimodal – GROVER 19 https://grover.allenai.org/
- 22. Harvard (1839) – Harvard – Harvard – Harvard – MIT – Northwestern – UIUC – WUSL – CMU (2009) – IIITD (Nov 14, 2017) 22
- 23. Harvard (1839) – Harvard – Harvard – Harvard – MIT – Northwestern – UIUC – WUSL – CMU (2009) – IIITD (April 25, 2019) 23
- 24. Pittsburgh Shooting / Gab post 24
- 25. Hate speech 25
- 29. De-duplicating audience Social audience = 437,632 + 153,000 + 805,097 or less?? 29
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- 32. Heuristic Identity Search 32 cerc.iiitd.ac.in Profile Content Self-mention Network Syntactic and Image Search Linking If self-identified / returned by more than one search method No Yes Candidate Identities name, location, username mobile no, post, friends, followers Paridhi Jain, Ponnurangam Kumaraguru, and Anupam Joshi. 2013. @I seek ‘fb.me’: Identifying Users across Multiple Online Social Networks. In Proceedings of the 22nd International Conference on World Wide Web, WWW ’13 Companion. ACM, New York, NY, USA, 1259- 1268. DOI=http://dx.doi.org/10.1145/2487788.2488160 [Honorable Mention Award}
- 33. Harvard (1839) – Harvard – Harvard – Harvard – MIT – Northwestern – UIUC – WUSL – CMU (2009) – IIITD (2016) 33
- 36. To understand users / customers – “Show me who your friends are and I’ll tell you who you are?” 36
- 37. To understand users / customers – “Show me who your friends are and I’ll tell you who you are?” 37
- 38. To understand users / customers – “Show me who your friends are and I’ll tell you who you are?” 38
- 39. To understand users / customers – Interest graph 39
- 40. 40 Selfies vs Potential KillFies
- 41. 41 Selfies vs Potential KillFies
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- 43. 439th International Workshop on Modeling Social Media (MSM), 2018 @ WWW
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- 46. Sample 46
- 47. Takeaways – Social Media is a powerful tool – Business / Government can use the analysis to their benefits – You should use it for your benefits J -You tried doing analysis on your accounts? – Lots of challenges and opportunities! – Will be happy to interact more, if needed 47