TY - CHAP A1 - Abramova, Olga A1 - Batzel, Katharina A1 - Modesti, Daniela T1 - Coping and regulatory responses on social media during health crisis BT - a large-scale analysis T2 - Proceedings of the 55th Hawaii International Conference on System Sciences N2 - 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. KW - Digital-Enabled Human-Information Interaction KW - big data KW - data mining KW - health crisis KW - social media Y1 - 2022 SN - 978-0-9981331-5-7 PB - HICSS Conference Office University of Hawaii at Manoa CY - Honolulu ER - TY - JOUR A1 - Blickensdörfer, Lukas A1 - Schwieder, Marcel A1 - Pflugmacher, Dirk A1 - Nendel, Claas A1 - Erasmi, Stefan A1 - Hostert, Patrick T1 - Mapping of crop types and crop sequences with combined time series of Sentinel-1, Sentinel-2 and Landsat 8 data for Germany JF - Remote sensing of environment : an interdisciplinary journal N2 - Monitoring agricultural systems becomes increasingly important in the context of global challenges like climate change, biodiversity loss, population growth, and the rising demand for agricultural products. High-resolution, national-scale maps of agricultural land are needed to develop strategies for future sustainable agriculture. However, the characterization of agricultural land cover over large areas and for multiple years remains challenging due to the locally diverse and temporally variable characteristics of cultivated land. We here propose a workflow for generating national agricultural land cover maps on a yearly basis that accounts for varying environmental conditions. We tested the approach by mapping 24 agricultural land cover classes in Germany for the three years 2017, 2018, and 2019, in which the meteorological conditions strongly differed. We used a random forest classifier and dense time series data from Sentinel-2 and Landsat 8 in combination with monthly Sentinel-1 composites and environmental data and evaluated the relative importance of optical, radar, and environmental data. Our results show high overall accuracy and plausible class accuracies for the most dominant crop types across different years despite the strong inter-annual meteorological variability and the presence of drought and nondrought years. The maps show high spatial consistency and good delineation of field parcels. Combining optical, SAR, and environmental data increased overall accuracies by 6% to 10% compared to single sensor approaches, in which optical data outperformed SAR. Overall accuracy ranged between 78% and 80%, and the mapped areas aligned well with agricultural statistics at the regional and national level. Based on the multi-year dataset we mapped major crop sequences of cereals and leaf crops. Most crop sequences were dominated by winter cereals followed by summer cereals. Monocultures of summer cereals were mainly revealed in the Northwest of Germany. We showcased that high spatial and thematic detail in combination with annual mapping will stimulate research on crop cycles and studies to assess the impact of environmental policies on management decisions. Our results demonstrate the capabilities of integrated optical time series and SAR data in combination with variables describing local and seasonal environmental conditions for annual large-area crop type mapping. KW - agricultural land cover KW - analysis-ready data KW - time series KW - large-area mapping KW - optical remote sensing KW - SAR KW - big data KW - multi-sensor Y1 - 2022 U6 - https://doi.org/10.1016/j.rse.2021.112831 SN - 0034-4257 SN - 1879-0704 VL - 269 PB - Elsevier CY - New York ER - TY - JOUR A1 - Hagemann, Linus A1 - Abramova, Olga T1 - Sentiment, we-talk and engagement on social media BT - insights from Twitter data mining on the US presidential elections 2020 JF - Internet research N2 - 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. KW - social media KW - engagement KW - data mining KW - big data Y1 - 2023 U6 - https://doi.org/10.1108/INTR-12-2021-0885 SN - 1066-2243 VL - 33 IS - 6 SP - 2058 EP - 2085 PB - Emeral CY - Bingley ER - TY - CHAP A1 - Hagemann, Linus A1 - Abramova, Olga T1 - Crafting audience engagement in social media conversations BT - evidence from the U.S. 2020 presidential elections T2 - Proceedings of the 55th Hawaii International Conference on System Sciences N2 - 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. KW - mediated conversation KW - big data KW - engagement KW - sentiment analysis KW - social media Y1 - 2022 SN - 978-0-9981331-5-7 SP - 3222 EP - 3231 PB - HICSS Conference Office University of Hawaii at Manoa CY - Honolulu ER - TY - JOUR A1 - Kaitoua, Abdulrahman A1 - Rabl, Tilmann A1 - Markl, Volker T1 - A distributed data exchange engine for polystores JF - Information technology : methods and applications of informatics and information technology JF - Information technology : Methoden und innovative Anwendungen der Informatik und Informationstechnik N2 - 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. KW - distributed systems KW - data migration KW - data transformation KW - big data KW - engine KW - data integration Y1 - 2020 U6 - https://doi.org/10.1515/itit-2019-0037 SN - 1611-2776 SN - 2196-7032 VL - 62 IS - 3-4 SP - 145 EP - 156 PB - De Gruyter CY - Berlin ER - TY - JOUR A1 - Kohler, Ulrich A1 - Kreuter, Frauke A1 - Stuart, Elizabeth A. T1 - Nonprobability Sampling and Causal Analysis JF - Annual review of statistics and its application N2 - 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. KW - causal inference KW - generalizability KW - self-selection KW - nonprobability sampling KW - validity KW - measurement error KW - heterogeneous treatment effects KW - big data Y1 - 2018 U6 - https://doi.org/10.1146/annurev-statistics-030718-104951 SN - 2326-8298 SN - 2326-831X VL - 6 SP - 149 EP - 172 PB - Annual Reviews CY - Palo Alto ER -