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  • Impact of Social Sciences – New media, familiar dynamics: academic hierarchies influence academics’ following behaviour on Twitter[show details]

    For what reasons do academics follow one another on Twitter? Robert Jäschke, Stephanie B. Linek and Christian P. Hoffmann analysed the Twitter activity of computer scientists and found that while the quality of information provided by a Twitter account is a key motive for following academic colleagues, there is also evidence of a career planning motive. As well as there being reciprocal following between users of the same academic status (except, remarkably, between PhD researchers), a form of strategic politeness can be observed whereby users follow those of higher academic status without necessarily being followed back. The emerging academic public sphere facilitated by Twitter is largely shaped by dynamics and hierarchies all too familiar to researchers struggling to plot their careers in academia.

  • Altmetric – What do computer scientists tweet? Analyzing the link-sharing practice on Twitter
  • URLs from tweets for a 2014 sample of Twitter users and for a set of computer scientists | Zenodo[show details]

    The files in this dataset are used to analyse the tweeting behaviour of computer scientists on Twitter. They comprise a set of 989,529 tweet-URL pairs (tweets_2014_researcher.tsv.bz2) from 2014 from 6,271 users of the computer scientists sample in specified by time, tweet id, user id, and URL, a set of 300,053,850 tweet ids (tweets_2014_sample.tsv.bz2) from the 1% Twitter stream sample from 2014, a set of 605,080 tweet-URL pairs (tweets_2014_sample_6694_users.tsv.bz2) from the 1% Twitter stream sample from 2014 for 6,694 users specified by time, tweet id, user id, and URL, a set of the top 10,000 host names (MAG_hosts_10000.tsv) from the Microsoft Academic Graph data (, specified by rank, URL count, and host name, and a set of 340 host names of URL shortening services (url_shortening_services.tsv). In addition, the following rankings (based on the odds ratio) of domains, hosts, and URLs that appear in both the researcher dataset and the sample are included: domains_by_odds_ratio.tsv.bz2 - a ranking of 61,860 domains, hosts_by_odds_ratio.tsv.bz2 - a ranking of 80,384 hosts, publisher_domains_by_odds_ratio.tsv.bz2 - a ranking of 924 publisher domains, publisher_urls_by_odds_ratio.tsv.bz2 - a ranking of 4,227 publisher URLs.

Recently added publications

Citation format:
Learning Semantic Relatedness from Human Feedback Using Relative Relatedness Learning. ISWC'17. Year 2017.
MixedTrails: Bayesian hypothesis comparison on heterogeneous sequential data. In Data Mining and Knowledge Discovery, Year 2017.
Sedentary Behavior among National Elite Rowers during Off-Training—A Pilot Study. In Frontiers in Physiology, (8) 655, Year 2017.