{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T02:36:45Z","timestamp":1760236605180,"version":"build-2065373602"},"reference-count":18,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2021,12,7]],"date-time":"2021-12-07T00:00:00Z","timestamp":1638835200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000185","name":"Defense Advanced Research Projects Agency","doi-asserted-by":"publisher","award":["W911NF-17-C-0099 and HR001121C0165"],"award-info":[{"award-number":["W911NF-17-C-0099 and HR001121C0165"]}],"id":[{"id":"10.13039\/100000185","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000183","name":"United States Army Research Office","doi-asserted-by":"publisher","award":["W911NF-16-1-0524"],"award-info":[{"award-number":["W911NF-16-1-0524"]}],"id":[{"id":"10.13039\/100000183","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>Online social media provides massive open-ended platforms for users of a wide variety of backgrounds, interests, and beliefs to interact and debate, facilitating countless discussions across a myriad of subjects. With numerous unique voices being lent to the ever-growing information stream, it is essential to consider how the types of conversations that result from a social media post represent the post itself. We hypothesize that the biases and predispositions of users cause them to react to different topics in different ways not necessarily entirely intended by the sender. In this paper, we introduce a set of unique features that capture patterns of discourse, allowing us to empirically explore the relationship between a topic and the conversations it induces. Utilizing \u201cmicroscopic\u201d trends to describe \u201cmacroscopic\u201d phenomena, we set a paradigm for analyzing information dissemination through the user reactions that arise from a topic, eliminating the need to analyze the involved text of the discussions. Using a Reddit dataset, we find that our features not only enable classifiers to accurately distinguish between content genre, but also can identify more subtle semantic differences in content under a single topic as well as isolating outliers whose subject matter is substantially different from the norm.<\/jats:p>","DOI":"10.3390\/e23121642","type":"journal-article","created":{"date-parts":[[2021,12,7]],"date-time":"2021-12-07T09:52:29Z","timestamp":1638870749000},"page":"1642","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Characterizing Topics in Social Media Using Dynamics of Conversation"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2753-5481","authenticated-orcid":false,"given":"James","family":"Flamino","sequence":"first","affiliation":[{"name":"Department of Computer Science, Rensselaer Polytechnic Institute, Troy, NY 12180, USA"},{"name":"Department of Physics, Applied Physics, and Astronomy, Rensselaer Polytechnic Institute, Troy, NY 12180, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0549-501X","authenticated-orcid":false,"given":"Bowen","family":"Gong","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Rensselaer Polytechnic Institute, Troy, NY 12180, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Frederick","family":"Buchanan","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Rensselaer Polytechnic Institute, Troy, NY 12180, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0307-6743","authenticated-orcid":false,"given":"Boleslaw K.","family":"Szymanski","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Rensselaer Polytechnic Institute, Troy, NY 12180, USA"},{"name":"Department of Physics, Applied Physics, and Astronomy, Rensselaer Polytechnic Institute, Troy, NY 12180, USA"},{"name":"Spo\u0142eczna Akademia Nauk, Henryka Sienkiewicza 9, 90-113 \u0141\u00f3d\u017a, Poland"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,12,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Bogdanov, P., Busch, M., Moehlis, J., Singh, A.K., and Szymanski, B.K. (2013, January 25\u201328). The social media genome: Modeling individual topic-specific behavior in social media. Proceedings of the 2013 IEEE\/ACM International Conference on Advances in Social Networks Analysis and Mining, Niagara, ON, Canada.","DOI":"10.1145\/2492517.2492621"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Diakopoulos, N.A., and Shamma, D.A. (2010, January 10\u201315). Characterizing debate performance via aggregated twitter sentiment. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, Atlanta, GA, USA.","DOI":"10.1145\/1753326.1753504"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Horne, B.D., Adali, S., and Sikdar, S. (August, January 31). Identifying the social signals that drive online discussions: A case study of reddit communities. Proceedings of the 2017 26th International Conference on Computer Communication and Networks (ICCCN), Vancouver, BC, Canada.","DOI":"10.1109\/ICCCN.2017.8038388"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Castillo, C., El-Haddad, M., Pfeffer, J., and Stempeck, M. (2014, January 15\u201319). Characterizing the life cycle of online news stories using social media reactions. Proceedings of the 17th ACM Conference on Computer Supported Cooperative Work & Social Computing, Baltimore, MD, USA.","DOI":"10.1145\/2531602.2531623"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Dodds, P.S., Harris, K.D., Kloumann, I.M., Bliss, C.A., and Danforth, C.M. (2011). Temporal patterns of happiness and information in a global social network: Hedonometrics and Twitter. PLoS ONE, 6.","DOI":"10.1371\/journal.pone.0026752"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Lee, C., Kwak, H., Park, H., and Moon, S. (2010, January 26\u201330). Finding influentials based on the temporal order of information adoption in twitter. Proceedings of the 19th International Conference on World Wide Web, Raleigh, NC, USA.","DOI":"10.1145\/1772690.1772842"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Ramage, D., Dumais, S., and Liebling, D. (2010, January 23\u201326). Characterizing microblogs with topic models. Proceedings of the Fourth International AAAI Conference on Weblogs and Social Media, Washington, DC, USA.","DOI":"10.1609\/icwsm.v4i1.14026"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/2133360.2133363","article-title":"Isolation-based anomaly detection","volume":"6","author":"Liu","year":"2012","journal-title":"ACM Trans. Knowl. Discov. Data."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Flamino, J., and Szymanski, B.K. (August, January 29). A Reaction-Based Approach to Information Cascade Analysis. Proceedings of the 2019 28th International Conference on Computer Communication and Networks (ICCCN), Valencia, Spain.","DOI":"10.1109\/ICCCN.2019.8847096"},{"key":"ref_10","unstructured":"Hessel, J., Tan, C., and Lee, L. (2016, January 17\u201320). Science, AskScience, and BadScience: On the coexistence of highly related communities. Proceedings of the International AAAI Conference on Web and Social Media, Cologne, Germany."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Zhang, A., Culbertson, B., and Paritosh, P. (2017, January 15\u201318). Characterizing online discussion using coarse discourse sequences. Proceedings of the International AAAI Conference on Web and Social Media, Montreal, QC, Canada.","DOI":"10.1609\/icwsm.v11i1.14886"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"15697","DOI":"10.1038\/s41598-018-32571-3","article-title":"Entropy Measures of Human Communication Dynamics","volume":"8","author":"Kulisiewicz","year":"2018","journal-title":"Sci. Rep."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Friedman, J., Hastie, T., and Tibshirani, R. (2001). The Elements of Statistical Learning, Springer.","DOI":"10.1007\/978-0-387-21606-5"},{"key":"ref_14","unstructured":"Kaufman, L., and Rousseeuw, P.J. (2009). Finding Groups in Data: An Introduction to Cluster Analysis, John Wiley & Sons."},{"key":"ref_15","unstructured":"Heidemann, J., Klier, M., and Probst, F. (2010, January 12\u201315). Identifying key users in online social networks: A pagerank based approach. Proceedings of the ICIS 2010, St. Louis, MO, USA."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/1852102.1852106","article-title":"A similarity measure for indefinite rankings","volume":"28","author":"Webber","year":"2010","journal-title":"ACM Trans. Inf. Syst."},{"key":"ref_17","first-page":"993","article-title":"Latent dirichlet allocation","volume":"3","author":"Blei","year":"2003","journal-title":"J. Mach. Learn. Res."},{"key":"ref_18","first-page":"18","article-title":"Emotion and virality: What makes online content go viral?","volume":"5","author":"Berger","year":"2013","journal-title":"NIM Mark. Intell. 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