{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,20]],"date-time":"2026-05-20T09:05:28Z","timestamp":1779267928801,"version":"3.51.4"},"publisher-location":"New York, NY, USA","reference-count":42,"publisher":"ACM","license":[{"start":{"date-parts":[[2026,5,25]],"date-time":"2026-05-25T00:00:00Z","timestamp":1779667200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2026,5,26]]},"DOI":"10.1145\/3795766.3799749","type":"proceedings-article","created":{"date-parts":[[2026,5,20]],"date-time":"2026-05-20T07:50:03Z","timestamp":1779263403000},"page":"430-439","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Exploring Large-Scale Social Media Data Using Sentiment Analysis and Topic Modeling - A Case Study of Instagram Posts in Context of the 2024 German Government Crisis"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-5649-8384","authenticated-orcid":false,"given":"Jessica","family":"Stiegelmayer","sequence":"first","affiliation":[{"name":"Universit\u00e4t Regensburg, Regensburg, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-4365-2545","authenticated-orcid":false,"given":"Arabella","family":"Petz","sequence":"additional","affiliation":[{"name":"Universit\u00e4t Regensburg, Regensburg, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-7086-8053","authenticated-orcid":false,"given":"Leo","family":"Bruckm\u00fcller","sequence":"additional","affiliation":[{"name":"Universit\u00e4t Regensburg, Regensburg, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-7845-9511","authenticated-orcid":false,"given":"Jakob","family":"Fehle","sequence":"additional","affiliation":[{"name":"Universit\u00e4t Regensburg, Regensburg, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4754-7842","authenticated-orcid":false,"given":"Michael","family":"Achmann-Denkler","sequence":"additional","affiliation":[{"name":"Universit\u00e4t Regensburg, Regensburg, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7278-8595","authenticated-orcid":false,"given":"Christian","family":"Wolff","sequence":"additional","affiliation":[{"name":"Universit\u00e4t Regensburg, Regensburg, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2026,5,25]]},"reference":[{"key":"e_1_3_3_2_2_2","doi-asserted-by":"crossref","unstructured":"Michael Achmann and Christian Wolff. 2023. Policy issues vs. documentation: Using bertopic to gain insight in the political communication in instagram stories and posts during the 2021 german federal election campaign. Digital Humanities in the Nordic and Baltic Countries Publications 5 1 (2023) 11\u201328.","DOI":"10.5617\/dhnbpub.10647"},{"key":"e_1_3_3_2_3_2","unstructured":"Alienmaster. n. d.. German Politicians Twitter Sentiment [Dataset]. https:\/\/huggingface.co\/datasets\/Alienmaster\/german_politicians_twitter_sentiment. Hugging Face. Accessed on March 1 2025."},{"key":"e_1_3_3_2_4_2","doi-asserted-by":"crossref","unstructured":"Jennifer Bast. 2021. Politicians Parties and Government Representatives on Instagram: A Review of Research Approaches Usage Patterns and Effects. Review of Communication Research 9 (July 2021). https:\/\/www.rcommunicationr.org\/index.php\/rcr\/article\/view\/108","DOI":"10.12840\/ISSN.2255-4165.032"},{"key":"e_1_3_3_2_5_2","doi-asserted-by":"publisher","unstructured":"Axel Bruns. 2019. After the \u2018APIcalypse\u2019: Social Media Platforms and Their Fight Against Critical Scholarly Research. Information Communication & Society 22 11 (2019) 1544\u20131566. 10.1080\/1369118X.2019.1637447","DOI":"10.1080\/1369118X.2019.1637447"},{"key":"e_1_3_3_2_6_2","unstructured":"Bundeszentrale f\u00fcr politische Bildung (bpb). 2023. Novemberpogrom 1938. https:\/\/www.bpb.de\/kurz-knapp\/hintergrund-aktuell\/542301\/novemberpogrom-1938\/. Accessed April 14 2025."},{"key":"e_1_3_3_2_7_2","unstructured":"Santiago Castro. 2017. Fast Krippendorff: Fast computation of Krippendorff\u2019s alpha agreement measure. GitHub github.com\/pln-fing-udelar\/fast-krippendorff (2017)."},{"key":"e_1_3_3_2_8_2","doi-asserted-by":"crossref","unstructured":"Priyavrat Chauhan Nonita Sharma and Geeta Sikka. 2021. The emergence of social media data and sentiment analysis in election prediction. Journal of Ambient Intelligence and Humanized Computing 12 (2021) 2601\u20132627.","DOI":"10.1007\/s12652-020-02423-y"},{"key":"e_1_3_3_2_9_2","doi-asserted-by":"publisher","unstructured":"Anna De\u00a0Fina. 2024. Bonding with followers: Chronotopes and scales in political communication on Instagram. Discourse studies (Sept. 2024). 10.1177\/14614456241276717","DOI":"10.1177\/14614456241276717"},{"key":"e_1_3_3_2_10_2","unstructured":"Deepset. n. d.. German BERT base [Machine Learning Model]. https:\/\/huggingface.co\/deepset\/gbert-base. Hugging Face. Accessed on March 3 2025."},{"key":"e_1_3_3_2_11_2","doi-asserted-by":"crossref","unstructured":"Zulfadzli Drus and Haliyana Khalid. 2019. Sentiment analysis in social media and its application: Systematic literature review. Procedia Computer Science 161 (2019) 707\u2013714.","DOI":"10.1016\/j.procs.2019.11.174"},{"key":"e_1_3_3_2_12_2","unstructured":"Catalina Goanta Thales Bertaglia and Adriana Iamnitchi. 2022. The Case for a Legal Compliance API for the Enforcement of the EU\u2019s Digital Services Act on Social Media Platforms. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/2205.06666. https:\/\/arxiv.org\/abs\/2205.06666"},{"key":"e_1_3_3_2_13_2","unstructured":"Maarten Grootendorst. 2022. BERTopic: Neural topic modeling with a class-based TF-IDF procedure. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/2203.05794 (2022)."},{"key":"e_1_3_3_2_14_2","unstructured":"Maarten Grootendorst. 2024. BERTopic - FAQ. https:\/\/maartengr.github.io\/BERTopic\/faq.html. Accessed April 11 2025."},{"key":"e_1_3_3_2_15_2","unstructured":"Maarten Grootendorst. n. d.. BERTopic on Large Datasets. https:\/\/colab.research.google.com\/drive\/1W7aEdDPxC29jP99GGZphUlqjMFFVKtBC?usp=sharing. Google Colab Notebook."},{"key":"e_1_3_3_2_16_2","unstructured":"Guhr Oliver. n. d.. German Sentiment Bert [Machine Learning Model]. https:\/\/huggingface.co\/oliverguhr\/german-sentiment-bert. Hugging Face. Accessed on March 1 2025."},{"key":"e_1_3_3_2_17_2","first-page":"84","volume-title":"Proceedings of the 6th international conference on natural language and speech processing (ICNLSP 2023)","author":"Hellwig Nils\u00a0Constantin","year":"2023","unstructured":"Nils\u00a0Constantin Hellwig, Markus Bink, Thomas Schmidt, Jakob Fehle, and Christian Wolff. 2023. Transformer-based analysis of sentiment towards german political parties on twitter during the 2021 election year. In Proceedings of the 6th international conference on natural language and speech processing (ICNLSP 2023). 84\u201398."},{"key":"e_1_3_3_2_18_2","doi-asserted-by":"crossref","unstructured":"Nils\u00a0Constantin Hellwig Jakob Fehle Markus Bink Thomas Schmidt and Christian Wolff. 2024. Exploring Twitter discourse with BERTopic: topic modeling of tweets related to the major German parties during the 2021 German federal election. International Journal of Speech Technology 27 4 (2024) 901\u2013921.","DOI":"10.1007\/s10772-024-10142-4"},{"key":"e_1_3_3_2_19_2","unstructured":"Hugging Face. n. d.. Fine-tune a pretrained model. https:\/\/huggingface.co\/docs\/transformers\/training. Hugging Face. Accessed on March 3 2025."},{"key":"e_1_3_3_2_20_2","doi-asserted-by":"publisher","unstructured":"Kamran Kowsari Kiana Jafari\u00a0Meimandi Mojtaba Heidarysafa Sanjana Mendu Laura Barnes and Donald Brown. 2019. Text Classification Algorithms: A Survey. Information 10 4 (2019). 10.3390\/info10040150","DOI":"10.3390\/info10040150"},{"key":"e_1_3_3_2_21_2","doi-asserted-by":"crossref","unstructured":"Caitlin Doogan\u00a0Poet Laureate Wray Buntine and Henry Linger. 2023. A systematic review of the use of topic models for short text social media analysis. Artificial Intelligence Review 56 12 (2023) 14223\u201314255.","DOI":"10.1007\/s10462-023-10471-x"},{"key":"e_1_3_3_2_22_2","doi-asserted-by":"publisher","unstructured":"Alessandro Ortis Giovanni\u00a0Maria Farinella and Sebastiano Battiato. 2020. Survey on visual sentiment analysis. IET Image Processing 14 8 (2020) 1440\u20131456. 10.1049\/iet-ipr.2019.1270","DOI":"10.1049\/iet-ipr.2019.1270"},{"key":"e_1_3_3_2_23_2","doi-asserted-by":"publisher","DOI":"10.5281\/zenodo.7525702"},{"key":"e_1_3_3_2_24_2","doi-asserted-by":"crossref","unstructured":"Yilang Peng. 2021. What makes politicians\u2019 Instagram posts popular? Analyzing social media strategies of candidates and office holders with computer vision. The International Journal of Press\/Politics 26 1 (2021) 143\u2013166.","DOI":"10.1177\/1940161220964769"},{"key":"e_1_3_3_2_25_2","doi-asserted-by":"publisher","DOI":"10.4337\/9781800376939.00021"},{"key":"e_1_3_3_2_26_2","doi-asserted-by":"crossref","unstructured":"Margarita Rodr\u00edguez-Ib\u00e1nez Antonio Cas\u00e1nez-Ventura F\u00e9lix Castej\u00f3n-Mateos and Pedro-Manuel Cuenca-Jim\u00e9nez. 2023. A review on sentiment analysis from social media platforms. Expert Systems with Applications 223 (2023) 119862.","DOI":"10.1016\/j.eswa.2023.119862"},{"key":"e_1_3_3_2_27_2","doi-asserted-by":"publisher","DOI":"10.1109\/SNAMS.2018.8554744"},{"key":"e_1_3_3_2_28_2","first-page":"74","volume-title":"Proceedings of the 18th conference on natural language processing (konvens 2022)","author":"Schmidt Thomas","year":"2022","unstructured":"Thomas Schmidt, Jakob Fehle, Maximilian Weissenbacher, Jonathan Richter, Philipp Gottschalk, and Christian Wolff. 2022. Sentiment analysis on twitter for the major German parties during the 2021 German federal election. In Proceedings of the 18th conference on natural language processing (konvens 2022). KONVENS 2022 Organizers, 74\u201387."},{"key":"e_1_3_3_2_29_2","unstructured":"Ssary. n. d.. XLM RoBERTa German sentiment [Machine Learning Model]. https:\/\/huggingface.co\/ssary\/XLM-RoBERTa-German-sentiment. Hugging Face. Accessed on March 1 2025."},{"key":"e_1_3_3_2_30_2","unstructured":"Stern. 2024. Grundsatzpapier: Christian Lindner fordert die Koalition heraus. https:\/\/www.stern.de\/politik\/deutschland\/grundsatzpapier\u2013christian-lindner-fordert-die-koalition-heraus-35192098.html. Accessed April 13 2025."},{"key":"e_1_3_3_2_31_2","unstructured":"TabularisAI. n. d.. Multilingual Sentiment Analysis [Machine Learning Model]. https:\/\/huggingface.co\/tabularisai\/multilingual-sentiment-analysis. Hugging Face. Accessed on March 1 2025."},{"key":"e_1_3_3_2_32_2","unstructured":"Tagesschau. 2023. Gewalt gegen Frauen: Ein Problem in Deutschland. https:\/\/www.tagesschau.de\/inland\/gesellschaft\/gewalt-gegen-frauen-116.html. Accessed April 14 2025."},{"key":"e_1_3_3_2_33_2","unstructured":"Tagesschau. 2024. Magdeburg: Weihnachtsmarkt in der Stadt. https:\/\/www.tagesschau.de\/inland\/gesellschaft\/magdeburg-weihnachtsmarkt-104.html. Accessed April 13 2025."},{"key":"e_1_3_3_2_34_2","unstructured":"Tagesschau. 2024. Pelicot-Prozess: Frankreichs Justiz vor der Herausforderung. https:\/\/www.tagesschau.de\/ausland\/europa\/frankreich-pelicot-prozess-100.html. Accessed April 14 2025."},{"key":"e_1_3_3_2_35_2","unstructured":"Tagesschau. 2024. Scholz verliert Vertrauensfrage. https:\/\/www.tagesschau.de\/inland\/scholz-verliert-vertrauensfrage-100.html. Accessed April 13 2025."},{"key":"e_1_3_3_2_36_2","doi-asserted-by":"publisher","DOI":"10.1145\/3342220.3343657"},{"key":"e_1_3_3_2_37_2","doi-asserted-by":"crossref","unstructured":"Ike Vayansky and Sathish\u00a0AP Kumar. 2020. A review of topic modeling methods. Information Systems 94 (2020) 101582.","DOI":"10.1016\/j.is.2020.101582"},{"key":"e_1_3_3_2_38_2","doi-asserted-by":"publisher","unstructured":"Tommaso Venturini and Richard Rogers. 2019. \u201cAPI-Based Research\u201d or How can Digital Sociology and Journalism Studies Learn from the Facebook and Cambridge Analytica Data Breach. Digital Journalism 7 4 (April 2019) 532\u2013540. 10.1080\/21670811.2019.1591927","DOI":"10.1080\/21670811.2019.1591927"},{"key":"e_1_3_3_2_39_2","unstructured":"Wikipedia. 2024. Liste der Mitglieder des Deutschen Bundestages (20. Wahlperiode). https:\/\/de.wikipedia.org\/wiki\/Liste_der_Mitglieder_des_Deutschen_Bundestages_(20._Wahlperiode). Accessed December 16 2025."},{"key":"e_1_3_3_2_40_2","unstructured":"Michael Wojatzki Eugen Ruppert Sarah Holschneider Torsten Zesch and Chris Biemann. 2017. Germeval 2017: Shared task on aspect-based sentiment in social media customer feedback. Proceedings of the GermEval (2017) 1\u201312."},{"key":"e_1_3_3_2_41_2","doi-asserted-by":"crossref","unstructured":"Chen Yang. 2021. Research in the Instagram context: Approaches and methods. The Journal of Social Sciences Research 7 1 (2021) 15\u201321.","DOI":"10.32861\/jssr.71.15.21"},{"key":"e_1_3_3_2_42_2","volume-title":"Ampel-Aus vorbereitet? FDP weist Vorwurf zur\u00fcck","year":"2024","unstructured":"ZDFheute. 2024. Ampel-Aus vorbereitet? FDP weist Vorwurf zur\u00fcck. https:\/\/www.zdf.de\/nachrichten\/politik\/deutschland\/fdp-ampel-bruch-vorbereitung-100.html Accessed: 2025-04-10."},{"key":"e_1_3_3_2_43_2","unstructured":"Zeit. 2024. Christian Lindner: Die Ampel-Aus aus der FDP-Bundesregierung. https:\/\/www.zeit.de\/politik\/deutschland\/2024-11\/christian-lindner-ampel-aus-fdp-bundesregierung\/komplettansicht. Accessed April 13 2025."}],"event":{"name":"WebSci '26: 18th ACM Web Science Conference 2026","location":"Braunschweig Germany","acronym":"WebSci '26","sponsor":["SIGWEB ACM Special Interest Group on Hypertext, Hypermedia, and Web"]},"container-title":["Proceedings of the 18th ACM Web Science Conference 2026"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3795766.3799749","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,20]],"date-time":"2026-05-20T08:06:35Z","timestamp":1779264395000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3795766.3799749"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,5,25]]},"references-count":42,"alternative-id":["10.1145\/3795766.3799749","10.1145\/3795766"],"URL":"https:\/\/doi.org\/10.1145\/3795766.3799749","relation":{},"subject":[],"published":{"date-parts":[[2026,5,25]]},"assertion":[{"value":"2026-05-25","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}