What Sticks With Whom? Twitter Follower-Followee Networks and News Classification

In this paper we analyze Twitter as a news channel in which the network of followers and followees significantly corre-sponds with the message content. We classified our data into twelve topics analogous to traditional newspaper sections and investigated whether the spread of information depended upon the Twitter network of followers and followees. To test this, we mapped the social network related to each topic and calculated the occurrence of retweet and mention messages whose senders and receivers were interconnected as followers and followees. We found that on average 10% of retweets (RT-messages) and 5% of direct mentions between users (AT-messages) in Twitter hashtags are sent and re-ceived by users interconnected as followers and followees. These figures vary considerably from topic to topic, ranging from 15%-19% within Technology, Special Events and Politics to 3%-5% within the categories Personalities and Twitter-Idioms. The results show that hard-news messages are retweeted by a considerably larger community of users interconnected as followers and followees. We then per-formed a statistical correlation analysis of the dataset to val-idate the classification of hashtag in news sections based on retweet connectivity.