%0 Book Section %1 weber2013political %A Weber, Ingmar %A Garimella, VenkataRamaKiran %A Teka, Asmelash %B Advances in Information Retrieval %D 2013 %I Springer Berlin Heidelberg %K hashtag k3 myown polarization political trends twitter %P 857-860 %R 10.1007/978-3-642-36973-5_102 %T Political Hashtag Trends %U http://dx.doi.org/10.1007/978-3-642-36973-5_102 %V 7814 %X Political Hashtag Trends (PHT) is an analysis tool for political left-vs.-right polarization of Twitter hashtags. PHT computes a leaning for trending, political hashtags in a given week, giving insights into the polarizing U.S. American issues on Twitter. The leaning of a hashtag is derived in two steps. First, users retweeting a set of “seed users” with a known political leaning, such as Barack Obama or Mitt Romney, are identified and the corresponding leaning is assigned to retweeters. Second, a hashtag is assigned a fractional leaning corresponding to which retweeting users used it. Non-political hashtags are removed by requiring certain hashtag co-occurrence patterns. PHT also offers functionality to put the results into context. For example, it shows example tweets from different leanings, it shows historic information and it links to the New York Times archives to explore a topic in depth. In this paper, we describe the underlying methodology and the functionality of the demo. %@ 978-3-642-36972-8

@incollection{weber2013political, abstract = {Political Hashtag Trends (PHT) is an analysis tool for political left-vs.-right polarization of Twitter hashtags. PHT computes a leaning for trending, political hashtags in a given week, giving insights into the polarizing U.S. American issues on Twitter. The leaning of a hashtag is derived in two steps. First, users retweeting a set of “seed users” with a known political leaning, such as Barack Obama or Mitt Romney, are identified and the corresponding leaning is assigned to retweeters. Second, a hashtag is assigned a fractional leaning corresponding to which retweeting users used it. Non-political hashtags are removed by requiring certain hashtag co-occurrence patterns. PHT also offers functionality to put the results into context. For example, it shows example tweets from different leanings, it shows historic information and it links to the New York Times archives to explore a topic in depth. In this paper, we describe the underlying methodology and the functionality of the demo.}, added-at = {2013-07-10T14:23:50.000+0200}, author = {Weber, Ingmar and Garimella, VenkataRamaKiran and Teka, Asmelash}, biburl = {https://www.bibsonomy.org/bibtex/2fdf213654412f58a39008ea3408f7579/asmelash}, booktitle = {Advances in Information Retrieval}, description = {Political Hashtag Trends - Springer}, doi = {10.1007/978-3-642-36973-5_102}, interhash = {9f236b167e684bbd0f5335beadf2e79e}, intrahash = {fdf213654412f58a39008ea3408f7579}, isbn = {978-3-642-36972-8}, keywords = {hashtag k3 myown polarization political trends twitter}, pages = {857-860}, publisher = {Springer Berlin Heidelberg}, series = {Lecture Notes in Computer Science}, timestamp = {2017-02-08T17:29:17.000+0100}, title = {Political Hashtag Trends}, url = {http://dx.doi.org/10.1007/978-3-642-36973-5_102}, volume = 7814, year = 2013 }