Authority-Based Conversation Tracking in Twitter: An Unattended Methodological Approach
Source abstract
Abstract
Twitter is undoubtedly one of the most widely used data sources to analyze human communication. The literature is full of examples where Twitter is accessed, and data are downloaded as the previous step to a more in-depth analysis in a wide variety of knowledge areas. Unfortunately, the extraction of relevant information from the opinions that users freely express in Twitter is complicated, both because of the volume generated—more than 6000 tweets per second—and the difficulties related to filtering out only what is pertinent to our research. Inspired by the fact that a large part of users use Twitter to communicate or receive political information, we created a method that allows for the monitoring of a set of users (which we will call authorities) and the tracking of the information published by them about an event. Our approach consists of dynamically and automatically monitoring the hottest topics among all the conversations where the authorities are involved, and retrieving the tweets in connection with those topics, filtering other conversations out. Although our case study involves the method being applied to the political discussions held during the Spanish general, local, and European elections of April/May 2019, the method is equally applicable to many other contexts, such as sporting events, marketing campaigns, or health crises.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Social Media and Politics · Misinformation and Its Impacts · Complex Network Analysis Techniques
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Computer Science Department, Universidad de Alcalá, 28801 Alcalá de Henares, Madrid, Spain
References (40)
Show source-provided references
1. News recommendations from social media opinion leaders: Effects on media trust and information seeking10.1111/jcc4.12127 ↗
2. Fang, Y., Chen, X., Song, Z., Wang, T., and Cao, Y. (2020). Modelling propagation of public opinions on microblogging big data using sentiment analysis and compartmental models. Natural Language Processing: Concepts, Methodologies, Tools, and Applications, IGI Global.10.4018/978-1-7998-0951-7.ch045 ↗
3. Gadek, G., Pauchet, A., Malandain, N., Vercouter, L., Khelif, K., Brunessaux, S., and Grilhères, B. (2018). Topological and topical characterisation of Twitter user communities. Data Technol. Appl., 52.10.1108/DTA-01-2018-0006 ↗
4. Twitter as a tool for and object of political and electoral activity: Considering electoral context and variance among actors10.1080/19331681.2017.1308289 ↗
5. How social media facilitates political protest: Information, motivation, and social networks10.1111/pops.12478 ↗
6. Social media and political engagement
7. Does Twitter motivate involvement in politics? Tweeting, opinion leadership, and political engagement10.1016/j.chb.2013.01.044 ↗
8. Young people, social media and connective action: From organisational maintenance to everyday political talk10.1080/13676261.2014.933198 ↗
9. Social media and fake news in the 2016 election10.1257/jep.31.2.211 ↗
10. Social network sites: Definition, history, and scholarship10.1111/j.1083-6101.2007.00393.x ↗
11. Tracking opinion over time: A method for reducing sampling error10.1086/297710 ↗
12. Yang, Y., Ault, T., Pierce, T., and Lattimer, C.W. (2000, January 24–28). Improving text categorization methods for event tracking. Proceedings of the 23rd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, Athens, Greece.10.1145/345508.345550 ↗
13. Yang, Y., Pierce, T., and Carbonell, J. (1998, January 24–28). A study of retrospective and on-line event detection. Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, Melbourne, Australia.10.1145/290941.290953 ↗
14. Allan, J., Carbonell, J.G., Doddington, G., Yamron, J., and Yang, Y. (1998, January 8–11). Topic Detection and Tracking Pilot Study Final Report. Proceedings of the DARPA Broadcast News Transcription and Understanding Workshop, Lansdowne, VA, USA.
15. Mønsted, B., Sapieżyński, P., Ferrara, E., and Lehmann, S. (2017). Evidence of complex contagion of information in social media: An experiment using Twitter bots. PLoS ONE, 12.10.1371/journal.pone.0184148 ↗
16. Hendrickson, S., Kolb, J., Lehman, B., and Montague, J. (2020, May 02). Trend Detection in Social Data. Twitter Blog. Available online: https://blog.twitter.com/en_us/a/2015/trend-detection-social-data.html.
17. Allan, J. (2002). Introduction to topic detection and tracking. Topic Detection and Tracking, Springer.10.1007/978-1-4615-0933-2 ↗
18. Bursty and hierarchical structure in streams10.1023/A:1024940629314 ↗
19. Petrović, S., Osborne, M., and Lavrenko, V. (2010). Streaming first story detection with application to twitter. Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics, Association for Computational Linguistics.
20. Bruns, A., and Burgess, J. (2015). Twitter hashtags from ad hoc to calculated publics. Hashtag Publics: The Power and Politics of Discursive Networks, Peter Lang Inc.
21. D’heer, E., Verdergem, P., and De Grove, F. (2016, January 5–8). # MissingData: A methodological inquiry of the hashtag to collect data from Twitter. Proceedings of the AoIR Selected Papers 17th Annual Conference Association Internet Research, Berlin, Germany.
22. ‘Social media logic’ meets professional norms: Twitter hashtags usage by journalists and politicians10.1080/1369118X.2017.1301515 ↗
23. Fano, S., and Slanzi, D. (2017). Using Twitter data to monitor political campaigns and predict election results. International Conference on Practical Applications of Agents and Multi-Agent Systems, Springer.10.1007/978-3-319-61578-3_19 ↗
24. Understanding# WorldEnvironmentDay user opinions in Twitter: A topic-based sentiment analysis approach10.3390/ijerph15112537 ↗
25. Communicating on Twitter during a disaster: An analysis of tweets during Typhoon Haiyan in the Philippines10.1016/j.chb.2015.04.020 ↗
26. Predicting elections for multiple countries using Twitter and polls10.1109/MIS.2015.17 ↗
27. Trend analysis of news topics on twitter10.7763/IJMLC.2012.V2.139 ↗
28. Hip and trendy: Characterizing emerging trends on Twitter10.1002/asi.21489 ↗
29. Lau, J.H., Collier, N., and Baldwin, T. (2012, January 8–15). On-line trend analysis with topic models:# twitter trends detection topic model online. Proceedings of the COLING 2012, Mumbai, India.
30. Cheong, M., and Lee, V. (2009, January 3–4). Integrating web-based intelligence retrieval and decision-making from the twitter trends knowledge base. Proceedings of the 2nd ACM Workshop on Social Web Search and Mining, Hong Kong, China.10.1145/1651437.1651439 ↗
31. Emerging topic detection in twitter stream based on high utility pattern mining10.1016/j.eswa.2018.07.051 ↗
32. A rule dynamics approach to event detection in twitter with its application to sports and politics10.1016/j.eswa.2016.02.028 ↗
33. A framework for real-time Twitter data analysis10.1016/j.comcom.2015.09.021 ↗
34. A taxonomy and survey of grid resource management systems for distributed computing10.1002/spe.432 ↗
35. Morstatter, F., Pfeffer, J., Liu, H., and Carley, K.M. (2013, January 8–11). Is the sample good enough? comparing data from twitter’s streaming api with twitter’s firehose. Proceedings of the Seventh International AAAI Conference on Weblogs and Social Media, Cambridge, MA, USA.
36. Analysis of political discourse on twitter in the context of the 2016 US presidential elections10.1016/j.giq.2017.11.001 ↗
37. Lai, M., Patti, V., Ruffo, G., and Rosso, P. (2018). Stance evolution and twitter interactions in an italian political debate. International Conference on Applications of Natural Language to Information Systems, Springer.10.1007/978-3-319-91947-8_2 ↗
38. Howard, P.N., and Kollanyi, B. (2017). Bots, #Strongerin, and #Brexit: Computational Propaganda During the UK-EU Referendum. SSRN Electron. J.10.2139/ssrn.2798311 ↗
39. Howard, P.N., Bolsover, G., Kollanyi, B., Bradshaw, S., and Neudert, L.M. (2020, May 02). Junk News and Bots during the U.S. Election: What Were Michigan Voters Sharing Over Twitter?|The Computational Propaganda Project. Available online: http://275rzy1ul4252pt1hv2dqyuf.wpengine.netdna-cdn.com/wp-content/uploads/2017/07/2206.pdf.
40. New time-based model to identify the influential users in online social networks