Asmelash Teka Hadgu

For millions of users Twitter is an important communication platform, a social network, and a system for resource sharing. Likewise, scientists use Twitter to connect with other researchers, announce calls for papers, or share their thoughts. Filtering tweets, discovering other researchers, or finding relevant information on a topic of interest, however, is difficult since no directory of researchers on Twitter exists.

In this paper we present an approach to identify Twitter accounts of… Mehr anzeigen

For millions of users Twitter is an important communication platform, a social network, and a system for resource sharing. Likewise, scientists use Twitter to connect with other researchers, announce calls for papers, or share their thoughts. Filtering tweets, discovering other researchers, or finding relevant information on a topic of interest, however, is difficult since no directory of researchers on Twitter exists.

In this paper we present an approach to identify Twitter accounts of researchers and demonstrate its utility for the discipline of computer science. Based on a seed set of computer science conferences we collect relevant Twitter users which we can partially map to ground-truth data. The mapping is leveraged to learn a model for classifying the remaining. To gain first insights into how researchers use Twitter, we empirically analyze the identified users and compare their age, popularity, influence, and social network. Weniger anzeigen

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… Mehr anzeigen

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. Weniger anzeigen

We study the change in polarization of hashtags on Twitter over time and show that certain jumps in polarity are caused by "hijackers" engaged in a particular type of hashtag war.