Ranking Tweets Considering Trust and Relevance Srijith Ravikumar , Raju Balakrishnan, Subbarao Kambhampati srijith@asu.edu rajub@asu.edu rao@asu.edu - PowerPoint PPT Presentation

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Ranking Tweets Considering Trust and Relevance Srijith Ravikumar , Raju Balakrishnan, Subbarao Kambhampati srijith@asu.edu rajub@asu.edu rao@asu.edu. Future Work. Spread of false information reduces the usability of Microblogs . We Model the Tweet eco-system as a tri-layer graph.

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Ranking Tweets Considering Trust and Relevance Srijith Ravikumar , Raju Balakrishnan, Subbarao Kambhampati srijith@asu.edu rajub@asu.edu rao@asu.edu

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Ranking tweets considering trust and relevance srijith ravikumar raju balakrishnan subbarao kambhampati srijith asu edu rajub asu edu rao asu edu

Ranking Tweets Considering Trust and Relevance

SrijithRavikumar, Raju Balakrishnan, Subbarao Kambhampati

srijith@asu.edurajub@asu.edu rao@asu.edu

FutureWork

Spread of false information reduces the usability of Microblogs.

We Model the Tweet eco-system as a tri-layer graph.

CompletedWork

Future Work

Top-k Relevance Comparison

  • How do we rank tweets considering trustworthiness and relevance?

  • Surface web uses hyperlink analysis between the pages.

  • Twitter consider retweets as “links” between the tweets for ranking.

Build Implicit links between the tweets containing the same fact, and analyze the link-structure.

Tweeted URL

Tweeted By

Top-k Trust Comparison

Retweets are sparse, and often planted or passively retweeted.

  • Agreement-edge weights between the tweets are computed using the Soft TF-IDF with Jaro-Winkler similarity.

  • Ranking-score is equal to sum of the edge weights.

Followers

Hyperlinks


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