How to Find Orchestrated Trolls? A Case Study on Identifying Polarized Twitter Echo Chambers
<b>Background:</b> This study presents a graph-based, macro-scale, polarity-based, echo chamber detection approach for Twitter. Echo chambers are a concern as they can spread misinformation, and reinforce harmful stereotypes and biases in social networks. <b>Methods:</b> This...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-03-01
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| Series: | Computers |
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| Online Access: | https://www.mdpi.com/2073-431X/12/3/57 |
| _version_ | 1797612536724455424 |
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| author | Nane Kratzke |
| author_facet | Nane Kratzke |
| author_sort | Nane Kratzke |
| collection | DOAJ |
| description | <b>Background:</b> This study presents a graph-based, macro-scale, polarity-based, echo chamber detection approach for Twitter. Echo chambers are a concern as they can spread misinformation, and reinforce harmful stereotypes and biases in social networks. <b>Methods:</b> This study recorded the German-language Twitter stream over two months, recording about 6.7M accounts and their 75.5M interactions (33M retweets). This study focuses on retweet interaction patterns in the German-speaking Twitter stream and found that the greedy modularity maximization and HITS metric are the most effective methods for identifying echo chambers. <b>Results:</b> The purely structural detection approach identified an echo chamber (red community, 66K accounts) focused on a few topics with a triad of anti-Covid, right-wing populism and pro-Russian positions (very likely reinforced by Kremlin-orchestrated troll accounts). In contrast, a blue community (113K accounts) was much more heterogeneous and showed “normal” communication interaction patterns. <b>Conclusions:</b> The study highlights the effects of echo chambers as they can make political discourse dysfunctional and foster polarization in open societies. The presented results contribute to identifying problematic interaction patterns in social networks often involved in the spread of disinformation by problematic actors. It is important to note that not the content but only the interaction patterns would be used as a decision criterion, thus avoiding problematic content censorship. |
| first_indexed | 2024-03-11T06:42:35Z |
| format | Article |
| id | doaj.art-3c4ac469e9234ba9b29011d817f07ebf |
| institution | Directory Open Access Journal |
| issn | 2073-431X |
| language | English |
| last_indexed | 2024-03-11T06:42:35Z |
| publishDate | 2023-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Computers |
| spelling | doaj.art-3c4ac469e9234ba9b29011d817f07ebf2023-11-17T10:26:29ZengMDPI AGComputers2073-431X2023-03-011235710.3390/computers12030057How to Find Orchestrated Trolls? A Case Study on Identifying Polarized Twitter Echo ChambersNane Kratzke0Department for Electrical Engineering and Computer Science, Lübeck University of Applied Sciences, Mönkhofer Weg 239, 23562 Lübeck, Germany<b>Background:</b> This study presents a graph-based, macro-scale, polarity-based, echo chamber detection approach for Twitter. Echo chambers are a concern as they can spread misinformation, and reinforce harmful stereotypes and biases in social networks. <b>Methods:</b> This study recorded the German-language Twitter stream over two months, recording about 6.7M accounts and their 75.5M interactions (33M retweets). This study focuses on retweet interaction patterns in the German-speaking Twitter stream and found that the greedy modularity maximization and HITS metric are the most effective methods for identifying echo chambers. <b>Results:</b> The purely structural detection approach identified an echo chamber (red community, 66K accounts) focused on a few topics with a triad of anti-Covid, right-wing populism and pro-Russian positions (very likely reinforced by Kremlin-orchestrated troll accounts). In contrast, a blue community (113K accounts) was much more heterogeneous and showed “normal” communication interaction patterns. <b>Conclusions:</b> The study highlights the effects of echo chambers as they can make political discourse dysfunctional and foster polarization in open societies. The presented results contribute to identifying problematic interaction patterns in social networks often involved in the spread of disinformation by problematic actors. It is important to note that not the content but only the interaction patterns would be used as a decision criterion, thus avoiding problematic content censorship.https://www.mdpi.com/2073-431X/12/3/57social networkTwitterstructural analysisecho chamberdetectioncase study |
| spellingShingle | Nane Kratzke How to Find Orchestrated Trolls? A Case Study on Identifying Polarized Twitter Echo Chambers Computers social network structural analysis echo chamber detection case study |
| title | How to Find Orchestrated Trolls? A Case Study on Identifying Polarized Twitter Echo Chambers |
| title_full | How to Find Orchestrated Trolls? A Case Study on Identifying Polarized Twitter Echo Chambers |
| title_fullStr | How to Find Orchestrated Trolls? A Case Study on Identifying Polarized Twitter Echo Chambers |
| title_full_unstemmed | How to Find Orchestrated Trolls? A Case Study on Identifying Polarized Twitter Echo Chambers |
| title_short | How to Find Orchestrated Trolls? A Case Study on Identifying Polarized Twitter Echo Chambers |
| title_sort | how to find orchestrated trolls a case study on identifying polarized twitter echo chambers |
| topic | social network structural analysis echo chamber detection case study |
| url | https://www.mdpi.com/2073-431X/12/3/57 |
| work_keys_str_mv | AT nanekratzke howtofindorchestratedtrollsacasestudyonidentifyingpolarizedtwitterechochambers |