Haters gonna hate: job-related offenses in Twitter

In this paper, we aim at finding out which users are likely to publicly demonstrate frustration towards their jobs on the microblogging platform Twitter – we will call these users haters. We show that the profiles of haters have specific characteristics in terms of vocabulary and connections. The implications of these findings may be used for the development of an early alert system that can help users to think twice before they post potentially self-harming content.

Authors: Ricardo Kawase, Patrick Siehndel, and Eelco Herder

PDF: kawase-www2014

FireMe! Website: http://fireme.l3s.uni-hannover.de/

Characterizing high-impact features for content retention in social web applications

One of the core challenges of automatically creating Social Web summaries is to decide which posts to remember, i.e., to consider for summary inclusion and which to forget. Keeping everything would overwhelm the user and would also neglect the often intentionally ephemeral nature of Social Web posts. In this paper, we analyze high-impact features that characterize memorable posts as a first step for this selection process. Our work is based on a user evaluation for discovering human expectations towards content retention.

Authors: Kaweh Djafari Naini, Ricardo Kawase, Nattiya Kanhabua and Claudia Niederée

PDF: naini-www2013naini-www2014