Microblog Analyses and Recommendations

Microblogging services have become immensely important throughout the last years as they allow users to easily share their thoughts to the public. The most successful and popular microblogging platform is Twitter, which currently serves more than 140 million active users who publish about 340 million posts each day.

Despite the huge volume of tweets posted, this data hardly features structure in terms of categorization of tweets. The only structural information available are so-called hashtags which are a means to add simple keywords as a part of the tweet. However, as hashtags may be chosen freely by the users, the hashtag vocabulary is heterogeneous. Searches for hashtags in order to find tweets concerning a certain topic may result in a search result featuring low recall due to this heterogeneity of hashtags. Our research focuses on the development of recommender systems to support users by providing recommendations for suitable hashtags. Such a recommender system aims to add structure to microblog entries and hence, a more homogeneous set of hashtags enabling better search performance.

The possibility of reaching millions of users within these networks not only attracts standard users, but also cyber-criminals who abuse the networks by spreading spam. This is accomplished by either creating fake accounts, bots, cyborgs or by hacking and compromising accounts. Compromised accounts are subsequently used to spread spam in the name of their legitimate owner. Therefore, our work sets out to investigate how Twitter users react to having their account hacked and how they deal  with compromised accounts. Further, we are highly interested in the detection of hacked accounts by analyzing the content of the user's tweets.