@inproceedings{HagemannAbramova2022, author = {Hagemann, Linus and Abramova, Olga}, title = {Crafting audience engagement in social media conversations}, series = {Proceedings of the 55th Hawaii International Conference on System Sciences}, booktitle = {Proceedings of the 55th Hawaii International Conference on System Sciences}, publisher = {HICSS Conference Office University of Hawaii at Manoa}, address = {Honolulu}, isbn = {978-0-9981331-5-7}, pages = {3222 -- 3231}, year = {2022}, abstract = {Observing inconsistent results in prior studies, this paper applies the elaboration likelihood model to investigate the impact of affective and cognitive cues embedded in social media messages on audience engagement during a political event. Leveraging a rich dataset in the context of the 2020 U.S. presidential elections containing more than 3 million tweets, we found the prominence of both cue types. For the overall sample, positivity and sentiment are negatively related to engagement. In contrast, the post-hoc sub-sample analysis of tweets from famous users shows that emotionally charged content is more engaging. The role of sentiment decreases when the number of followers grows and ultimately becomes insignificant for Twitter participants with a vast number of followers. Prosocial orientation ("we-talk") is consistently associated with more likes, comments, and retweets in the overall sample and sub-samples.}, language = {en} } @inproceedings{AbramovaBatzelModesti2022, author = {Abramova, Olga and Batzel, Katharina and Modesti, Daniela}, title = {Coping and regulatory responses on social media during health crisis}, series = {Proceedings of the 55th Hawaii International Conference on System Sciences}, booktitle = {Proceedings of the 55th Hawaii International Conference on System Sciences}, publisher = {HICSS Conference Office University of Hawaii at Manoa}, address = {Honolulu}, isbn = {978-0-9981331-5-7}, pages = {10}, year = {2022}, abstract = {During a crisis event, social media enables two-way communication and many-to-many information broadcasting, browsing others' posts, publishing own content, and public commenting. These records can deliver valuable insights to approach problematic situations effectively. Our study explores how social media communication can be analyzed to understand the responses to health crises better. Results based on nearly 800 K tweets indicate that the coping and regulation foci framework holds good explanatory power, with four clusters salient in public reactions: 1) "Understanding" (problem-promotion); 2) "Action planning" (problem-prevention); 3) "Hope" (emotion-promotion) and 4) "Reassurance" (emotion-prevention). Second, the inter-temporal analysis shows high volatility of topic proportions and a shift from self-centered to community-centered topics during the course of the event. The insights are beneficial for research on crisis management and practicians who are interested in large-scale monitoring of their audience for well-informed decision-making.}, language = {en} } @article{HagemannAbramova2023, author = {Hagemann, Linus and Abramova, Olga}, title = {Sentiment, we-talk and engagement on social media}, series = {Internet research}, volume = {33}, journal = {Internet research}, number = {6}, publisher = {Emeral}, address = {Bingley}, issn = {1066-2243}, doi = {10.1108/INTR-12-2021-0885}, pages = {2058 -- 2085}, year = {2023}, abstract = {Purpose Given inconsistent results in prior studies, this paper applies the dual process theory to investigate what social media messages yield audience engagement during a political event. It tests how affective cues (emotional valence, intensity and collective self-representation) and cognitive cues (insight, causation, certainty and discrepancy) contribute to public engagement. Design/methodology/approach The authors created a dataset of more than three million tweets during the 2020 United States (US) presidential elections. Affective and cognitive cues were assessed via sentiment analysis. The hypotheses were tested in negative binomial regressions. The authors also scrutinized a subsample of far-famed Twitter users. The final dataset, scraping code, preprocessing and analysis are available in an open repository. Findings The authors found the prominence of both affective and cognitive cues. For the overall sample, negativity bias was registered, and the tweet's emotionality was negatively related to engagement. In contrast, in the sub-sample of tweets from famous users, emotionally charged content produced higher engagement. The role of sentiment decreases when the number of followers grows and ultimately becomes insignificant for Twitter participants with many followers. Collective self-representation ("we-talk") is consistently associated with more likes, comments and retweets in the overall sample and subsamples. Originality/value The authors expand the dominating one-sided perspective to social media message processing focused on the peripheral route and hence affective cues. Leaning on the dual process theory, the authors shed light on the effectiveness of both affective (peripheral route) and cognitive (central route) cues on information appeal and dissemination on Twitter during a political event. The popularity of the tweet's author moderates these relationships.}, language = {en} } @article{KaitouaRablMarkl2020, author = {Kaitoua, Abdulrahman and Rabl, Tilmann and Markl, Volker}, title = {A distributed data exchange engine for polystores}, series = {Information technology : methods and applications of informatics and information technology}, volume = {62}, journal = {Information technology : methods and applications of informatics and information technology}, number = {3-4}, publisher = {De Gruyter}, address = {Berlin}, issn = {1611-2776}, doi = {10.1515/itit-2019-0037}, pages = {145 -- 156}, year = {2020}, abstract = {There is an increasing interest in fusing data from heterogeneous sources. Combining data sources increases the utility of existing datasets, generating new information and creating services of higher quality. A central issue in working with heterogeneous sources is data migration: In order to share and process data in different engines, resource intensive and complex movements and transformations between computing engines, services, and stores are necessary. Muses is a distributed, high-performance data migration engine that is able to interconnect distributed data stores by forwarding, transforming, repartitioning, or broadcasting data among distributed engines' instances in a resource-, cost-, and performance-adaptive manner. As such, it performs seamless information sharing across all participating resources in a standard, modular manner. We show an overall improvement of 30 \% for pipelining jobs across multiple engines, even when we count the overhead of Muses in the execution time. This performance gain implies that Muses can be used to optimise large pipelines that leverage multiple engines.}, language = {en} } @article{KohlerKreuterStuart2018, author = {Kohler, Ulrich and Kreuter, Frauke and Stuart, Elizabeth A.}, title = {Nonprobability Sampling and Causal Analysis}, series = {Annual review of statistics and its application}, volume = {6}, journal = {Annual review of statistics and its application}, publisher = {Annual Reviews}, address = {Palo Alto}, issn = {2326-8298}, doi = {10.1146/annurev-statistics-030718-104951}, pages = {149 -- 172}, year = {2018}, abstract = {The long-standing approach of using probability samples in social science research has come under pressure through eroding survey response rates, advanced methodology, and easier access to large amounts of data. These factors, along with an increased awareness of the pitfalls of the nonequivalent comparison group design for the estimation of causal effects, have moved the attention of applied researchers away from issues of sampling and toward issues of identification. This article discusses the usability of samples with unknown selection probabilities for various research questions. In doing so, we review assumptions necessary for descriptive and causal inference and discuss research strategies developed to overcome sampling limitations.}, language = {en} }