Research Data

Permanent URI for this collectionhttps://www.weizenbaum-library.de/handle/id/977

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Subject: search.filters.subject.Twitter

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    How Influencers and Multipliers Drive Polarization and Issue Alignment on Twitter/X - Data (Version v1) [Data set]
    (2025) Pournaki, Armin; Gaisbauer, Felix; Olbrich, Eckehard
    We provide anonymized retweet networks extracted from trending topics in Germany collected between 2021 to 2023. More specifically, we collected tweets from 2021-03-29 to 2023-07-12 according to the following scheme: at the beginning of each day, we launched a script that collects the current "trending topics" (from now on referred to as "trends") in Germany using the Twitter Trend API (v1). By default, trends are personalized based on the account's Twitter/X usage. One can, however, disable the personalization by setting a specific location from which to draw the trending topics, which then yields "popular topics among people in a specific geographic location" (X/Twitter2025). We re-ran the script every 15 minutes. At the end of each day, we counted the number of times each trending topic appeared during the day and kept the top 5 most frequent ones. This gave us a proxy of the five most important trending topics for that day. We then used the Twitter Search API (v1) to collect German-speaking tweets using the exact trend keyword as a query on the day it trended and the day after (48hrs). All the tweets were collected using a single Twitter API key, collecting tweets for maximally 24 hours every day. For each trend, we extract a retweet network, in which nodes are Twitter users and a directed link is drawn from user to if retweets . We provide one retweet network for each trend as a csv after anonymizing the user_ids. There is one csv for each trend containing the columns source,target,weight. The filename contains the date and the keyword that was searched: T__.csv All the individual files are contained in rtn.zip. Additionally, we computed a topic model on the full text of tweets which allowed us to classify each trend into one larger metatopic (such as Covid, Climate Change, Sports, ...). This topic assignment is contained in trend2topic.csv. For more information on the topic model, please refer to the paper https://doi.org/10.1609/icwsm.v19i1.35890.
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    Supplemental material for “Datafication Markers: Curation and User Network Effects on Mobilization and Polarization During Elections”
    (2023) Gagrčin, Emilija dc.contributor.author Ohme, Jakob dc.contributor.author Buttgereit, Lina dc.contributor.author Grünewald, Felix
    Supplemental material Table A1. Predicting campaign participation (cross-sectional) Table A2. Predicting campaign participation (auto-regressive) Table A3. Predicting vote choice certainty (cross-sectional) Table A4. Predicting vote choice certainty (auto-regressive) Table A5. Predicting turnout (cross-sectional) Table A6. Predicting turnout (auto-regressive) Table A7. Predicting attitude reinforcement (change variable) Table A8. Predicting affective polarization (cross-sectional) Table A8. Predicting affective polarization (cross-sectional)
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    Supplementary Material for “Varieties of antigenderism: the politicization of gender issues across three European populist radical right parties”
    (2023) Reinhardt, Susanne; Heft, Annett; Pavan, Elena
    ### Supplement I: Table S1 Overview of all posts by page holders (Facebook and Twitter) Table S2 Reliability Scores Table S3 Frequency Table: General Topic across Parties (in %) in Baseline Corpus, coded as main topic Table S4 Frequency Table: Posts containing gender topics across parties (in %) in Baseline Corpus, not coded as main topic Table S5 Frequency Table: Position toward Gender Topic across parties (in %) Table S6 Node Centrality ### Supplement II: Cross-Country Dictionary Construction Keyword List Topic Network Construction Community Detection Illustrative Examples of Posts for the Most Frequent Gender Topics
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    Supplementary Material for “Same, same but different? Explaining issue agendas of right-wing parties’ Facebook campaigns to the 2019 EP election”
    (2023) Pfetsch, Barbara; Benert, Vivien; Heft, Annett
    Table 2: STM results: labels, topic proportions and example words
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    Supplementary Material for “Political Opinion Formation as Epistemic Practice. The Hashtag Assemblage of #metwo”
    (2020) Berg, Sebastian; König, Tim; Koster, Ann-Kathrin
    Appendix 1. German newspaper articles reporting on the hashtag #metwo. Appendix 2. Relative share of references (retweets, replies, quotes, mentions) between communities, rounded. References are column-directed and relative to the total number of references per community. Appendix 3. Share of topics within communities in percent, rounded.
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    Supplementary Material for “Mobilization and Support Structures in Radical Right Party Networks”
    (2022) Heft, Annett; Reinhardt, Susanne; Pfetsch, Barbara
    Supplementary Online Appendix Mobilization and Support Structures in Radical Right Party Networks. Digital Political Communication Ecologies in the 2019 European Parliament Elections Table A1: Data overview on parties’ activity and references included in study, May 2019 Table A2: Data overview on user engagements with parties and references included in study, May 2019 Table A3: Valence of parties’ digital connections (top-down), in percent Table A4: Valence of user interactions with parties, in percent Codebook A – Actors Codebook B – Functions of References
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    Supplementary Material for “Transnational issue agendas of the radical right? Parties’ Facebook campaign communication in six countries during the 2019 European Parliament election”
    (2023) Heft, Annett; Pfetsch, Barbara; Voskresenskii, Vadim; Benert, Vivien
    Supplemental online material. Appendix 1: Party backgrounds. Appendix 2: Summary statistics for candidate models with varying K
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    Data of the paper: Clickbait or conspiracy? How Twitter users address the epistemic uncertainty of a controversial preprint
    (Center for Open Science, 2022-06-22) Franzreb, Carlos; Schimmler, Sonja; Bauer, Mareike Fenja
    This project contains sources related with the paper „Clickbait or conspiracy? How Twitter users address the epistemic uncertainty of a controversial preprint“:
    Scripts
    Scripts used to retrieve Tweets and to analyze/visualize them.

    Quantitative Analysis: Data
    + tweets.json: All Tweets of the relevant users as nodes and their relationships (retweet, quote or reply) as edges.
    + users_clustered.json: Users as nodes and their follow-relationships as edges, clustered with the Leiden algorithm.
    + follower_network.json: JSON file corresponding to Figure 1.
    + interaction_network.json: JSON file corresponding to Figure 2

    Qualitative Analysis: Data
    Replies and quotes of the Tweets that are used in the qualitative analysis.