Titlebook: Social Informatics; 8th International Co Emma Spiro,Yong-Yeol Ahn Conference proceedings 2016 Springer International Publishing AG 2016 dec

Hiatal-Hernia

Lingzi Hong,Weiwei Yang,Philip Resnik,Vanessa Frias-Martinez

macrophage

Dmytro Karamshuk,Tetyana Lokot,Oleksandr Pryymak,Nishanth Sastry

inspiration

Tom Mirowski,Shoumik Roychoudhury,Fang Zhou,Zoran Obradovic

antecedence

The Social Dynamics of Language Change in Online Networksial ties are significantly better conduits of linguistic influence. Geographic locality appears to play a more limited role: we find relatively little evidence to support the hypothesis that individuals are more influenced by geographically local social ties, even in their usage of geographical dial

malign

Demonstrate

Comment-Profiler: Detecting Trends and Parasitic Behaviors in Online Commentselps identify three major groups of users based on their utilization (3, 9, 15 h of up-time). Second, we identify surprising and abnormal behaviors using our features. Interestingly, we find: (a) two tightly collaborative groups of size at least 29 users that seem to be promoting the same ideas, (b)

突袭

A Diffusion Model for Maximizing Influence Spread in Large Networkshe influential nodes identified by our model achieve significantly higher influence spread compared to other popular models. The model parameters can easily be learned from basic, readily available training data. In the absence of training, our approach can still be used to identify influential seed

轨道

Determining the Veracity of Rumours on Twitterak of the rumour. Such time-windows were key as they allowed useful insight into the progression of the rumours. From our findings, we identified that our model was significantly more accurate than similar studies in the literature. We also identified critical attributes of the data that give rise t

uncertain

Identifying Partisan Slant in News Articles and Twitter During Political Crisesles and Twitter posts can be inferred with a high level of accuracy. These findings allow us to better understand the dynamics of partisan opinion formation during mass crises and the interplay between mainstream and social media in such circumstances.

Trabeculoplasty

Predicting Poll Trends Using Twitter and Multivariate Time-Series Classificationre tracked on an hour-by-hour basis. Together these form multivariate time-series. One commonly used approach to this problem is based on the majority voting scheme. This method assumes the univariate time-series from different features have equal importance. To alleviate this issue a weighted shape