Jana Diesner

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Scholars have often relied on name initials to resolve name ambiguities in large-scale coauthorship network research. This approach bears the risk of incorrectly merging or splitting author identities. The use of initial-based disambiguation has been justified by the assumption that such errors would not affect research findings too much. This paper tests(More)
This paper shows empirically how the choice of certain data pre-processing methods for disambiguating author names affects our understanding of the structure and evolution of co-publication networks. Thirty years of publication records from 125 Information Systems journals were obtained from DBLP. Author names in the data were pre-processed via algorithmic(More)
Texts can be coded and analyzed as networks of concepts often referred to as maps or semantic networks. In such networks, for many texts, there are elements of social structure – the connections among people, organizations, events, and so on. Within organizational and social network theory an approach called the meta-matrix is used to describe social(More)
Applying the concept of triadic closure to coauthorship networks means that scholars are likely to publish a joint paper if they have previously coauthored with the same people. Prior research has identified moderate to high (20 to 40%) closure rates; suggesting this mechanism is a reasonable explanation for tie formation between future coauthors. We show(More)
In this paper, we evaluate the predictability of tweets associated with controversial versus non-controversial topics. As a first step, we crowd-sourced the scoring of a predefined set of topics on a Likert scale from non-controversial to controversial. Our feature set entails and goes beyond sentiment features, e.g., by leveraging empathic language and(More)
The popularity and availability of Twitter as a service and a data source have fueled the interest in sentiment analysis. Previous research has shed light on the challenges that contextualizing effects and linguistic complexities pose for the accurate sentiment classification of tweets. We test the effect of adding manually-annotated, corpus-based hashtags(More)
We present novel research at the intersection of review mining and impact assessment of issue-focused information products, namely documentary films. We develop and evaluate a theoretically grounded classification schema, related codebook, corpus annotation, and prediction model for detecting multiple types of impact that documentaries can have on(More)
1:00 – 2:00 p.m.  Presentation Session I YESWORKFLOW: MORE PROVENANCE MILEAGE FROM HYBRID PROVENANCE MODELS AND QUERIES Bertram Ludäscher (presenter); joint work with Duc Vu, Qiwen Wang, Yang Cao, Qian Zhang, Timothy McPhillips ALL AND EACH: THE DYNAMICS OF SCALE IN DIGITAL HERITAGE CULTURES Rhiannon Bettivia INFORMED CONSENT AND CHEMICAL EXPOSURE FROM(More)