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ControversyDetection

Main Reference: " Quantifying Controversy on Social Media "

Articles

Papers that Have been read until now:

  • Community Interaciton and Conflict on the Web (Behdad)
  • Political Discourse on Social Media: Echo Chambers, Gatekeepers, and the Price of Bipartisanship (Mehrdad)
  • A Long-Term Analysis of Polarization on Twitter (Mehrdad)
  • Reducing Controversy by Connecting Opposing Views (Behdad)
  • Network Structure and Community Evolution on Twitter: Human Behavior Change in Response to the 2011 Japanese Earthquake and Tsunami (Mehrdad)
  • The Bursty Dynamics of the Twitter Information Network (Behdad)
  • Quantifying Polarization on Twitter: The Kavanaugh Nomination (Mehrdad)
  • Content and Network Dynamics Behind Egyptian Political Polarization on Twitter (Behdad)
  • Analyzing Polarization of Social Media Users and News Sites during Political Campaigns (Mehrdad)
  • Stance Evolution and Twitter Interactions in an Italian Political Debate (Behdad)
  • Current State of Text Sentiment Analysis from Opinion to Emotion Mining(Behdad, Mehrdad)
  • Vocabulary-based Method for Quantifying Controversy in Social Media(Behdad)
  • Aggregate Characterization of User Behavior in Twitter and Analysis of the Retweet Graph(Mehrdad)
  • Annotating Agreement and Disagreement in Threaded Discussion (Behdad)
  • I Couldn’t Agree More: The Role of Conversational Structure in Agreement and Disagreement Detection in Online Discussions(Mehrdad)

Papers that would be read:

Resources

Tasks

Task that would be done:

  • Report degree distribution and other structure measures in reply, mention and retweet graph dynamically and report this on two part of the polarized network(Behdad)
  • Report plorization measures on mention and reply graph(Mehrdad)

Questions to be answered:

Future Ideas:

in progressing ideas for polarization quantifying:

  • Prediction Controversy
  • Heterogeneous Analysis
  • Sentiment Analysis on Users(add sentiment to weight of edges in mention graph)
  • Graph Prediction
  • Clustering Coeffient analysis on retweet graph
  • Training model on user's tweets in two partitions
  • infulence of news on sentiment dynamic analysis

Conferences