Detecting Political Leanings & Propagandists on Twitter

sirius-xm-twitter-politics

Detecting Political tweets based on hashtags: (single iteration proposed by Conover et al. can it be improved by multiple iterations?)

  1. start by labeling one popular/predictive hashtag from each camp manually.
  2. label new hashtags if they co-occur with the already labeled hashtags
  3. remove hashtags that co-occur below a threshold value, also manually remove the false positives.

Constructing communication networks:

  1. vertices of this network are tweeters of the political hashtags detected above.
  2. mention edge weights: number of mentions between the two users.
  3. retweet edge weights: number of retweets between the two users.

Clustering communication networks:

  1. starting with the retweet network constructed above, apply Newman’s modularity based clustering algorithm.
  2. cluster by label propagation method (Raghavan,2007): iteratively assign each node the label that is shared by most of its neighbors.

Mentions form a communication bridge across which information flows between ideologically-opposed users; whereas, people with similar ideologies tend to retweet exclusively each other’s messages, especially propagandists:

  1. First, label one known popular user from each camp.
  2. At each iteration relabel the users by argmax(assoc1,…, assocn) where associ is the ratio of users retweeted of campi or/∪ by campi. Stop after some iterations.
  3. If at least a fraction f of the connections are to users in the same cluster then the user is a hyperadvocate; otherwise, the user is neutral.

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