Sockpuppets in Online Discussions: Use and Abuse Srijan Kumar Jure - - PowerPoint PPT Presentation
Sockpuppets in Online Discussions: Use and Abuse Srijan Kumar Jure Leskovec Justin Cheng V.S. Subrahmanian An Army of Me: Sockpuppets in Online Discussion Communities. S. Kumar, J. Cheng, J. Leskovec and V.S. Subrahmanian. Proceedings of
An Army of Me: Sockpuppets in Online Discussion Communities. S. Kumar, J. Cheng, J. Leskovec and V.S. Subrahmanian. Proceedings of World Wide Web Conference, 2017 (WWW 2017). Best Paper Award Honorable Mention. Srijan Kumar Justin Cheng Jure Leskovec V.S. Subrahmanian
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Eric_17 April 28 2013, 12AM
have nothing other than “oh that’s your opinion” instead of coming up with their own argument
Fellstrike April 29 2013, 6PM
Quit talking to yourself, *******. Get back on your meds if you’re going to do that
bdiaz209 April 28 2013, 11PM
Possibly the best blog I’ve ever read major props to you
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Note: we use the IP addresses for definition, but not detection
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Smoothzilla Feb 5 2013, 3PM
Thanks for your support!!!!
Falcon-X32 Feb 5 2013, 3PM
I agree. You are absolutely right!
jakey008 Feb 5 2013, 2PM
should have read the reviews first :(
ricobeans27 Feb 5 2013, 3PM
Couldn’t agree more. Interact more with each other p < 10-3 Upvote each other more p < 10-3
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More simiar Less similar
Ordinary Sockpuppet 1 Sockpuppet 2
Similarity is measured as cosine similarity between user posts’ features: LIWC, sentiment, number of words, etc.
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Less similar More similar
Ordinary Sockpuppet 1 Sockpuppet 2
Similarity is measured as cosine similarity between user posts’ features: LIWC, sentiment, number of words, etc.
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Non-sockpuppet Sockpuppet 1 Sockpuppet 2
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Levenshtein distance between usernames Number of pairs
5 10 15 20 0 100 200 300
Sock pairs Random pairs
Hypothesis: Deceptive sockpuppets of the same master have very different usernames.
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srijan Feb 5 2013, 3PM
i agree.. these morons dont know a thing
theRealBatman Feb 5 2013, 3PM
YOU ARE STUPID AND A *****
srijan Feb 5 2013, 2PM
best article i have read!!!
ricobeans27 Feb 5 2013, 3PM
But this article doesn’t make any sense More opinionated p < 10-3 Swear more p < 10-3 Downvoted and reported more p < 10-3
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theRealBatman Feb 5 2013, 3PM
why so?
srijan Feb 5 2013, 3PM
best article ever! We quantify the amount of support by counting assenting, negation and dissenting words from LIWC
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theRealBatman Feb 5 2013, 3PM
Totally agree!!
srijan Feb 5 2013, 3PM
best article ever! We quantify the amount of support by counting assenting, negation and dissenting words from LIWC
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theRealBatman Feb 5 2013, 3PM
I don’t think so
srijan Feb 5 2013, 3PM
best article ever! We quantify the amount of support by counting assenting, negation and dissenting words from LIWC
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0.5 0.0 1.0
0.58 0.42
0.70 0.30
0.74 0.26 Pretender Non-pretender
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Number of words, characters, etc., LIWC counts, Readability, Sentiment, …
Number of upvotes and downvotes, Fraction of reported posts, Is account reported, …
Number of posts, number of replies, reciprocity of posts, age of account, …
Note: we are not using the IP based features
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0.5 0.6 0.8 1.0 0.7 0.9 0.57 0.54 0.59 0.68
Baseline
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0.5 0.6 0.8 1.0 0.7 0.9 0.80 0.56 0.86 0.91
Baseline
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– Data-Driven Approaches towards Malicious Behavior Modeling. Jiang et al., SIGKDD 2017 – Antisocial Behavior on the Web: Characterization and Detection. Kumar et al., WWW 2017
– Disinformation on the Web: Impact, Characterisitics and Detection of Wikipedia Hoaxes. Kumar et al., WWW 2016
– VEWS: A Wikipedia Vandal Early Warning System. Kumar et al., SIGKDD 2015
– Linguisitic Harbingers of Betrayal: A Case Study on an Online Strategic Game. Niculae et al., ACL 2015
– Accurately Detecting Trolls in Slashdot Zoo via Decluttering. Kumar et al., ASONAM 2014
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MIS2