3 research outputs found
Predicting Scientific Success Based on Coauthorship Networks
We address the question to what extent the success of scientific articles is
due to social influence. Analyzing a data set of over 100000 publications from
the field of Computer Science, we study how centrality in the coauthorship
network differs between authors who have highly cited papers and those who do
not. We further show that a machine learning classifier, based only on
coauthorship network centrality measures at time of publication, is able to
predict with high precision whether an article will be highly cited five years
after publication. By this we provide quantitative insight into the social
dimension of scientific publishing - challenging the perception of citations as
an objective, socially unbiased measure of scientific success.Comment: 21 pages, 2 figures, incl. Supplementary Materia
Emotional Divergence Influences Information Spreading in Twitter
We analyze data about the micro-blogging site Twitter using sentiment extraction techniques. From an information perspective, Twitter users are involved mostly in two processes: information creation and subsequent distribution (tweeting), and pure information distribution (retweeting), with pronounced preference to the first. However a rather substantial fraction of tweets are retweeted. Here, we address the role of the sentiment expressed in tweets for their potential aftermath. We find that although the overall sentiment (polarity) does not influence the probability of a tweet to be retweeted, a new measure called "emotional divergence" does have an impact. In general, tweets with high emotional diversity have a better chance of being retweeted, hence influencing the distribution of information