Opinion formation on social media: an empirical approach
Chaos (Woodbury, N.Y.), ISSN: 1089-7682, Vol: 24, Issue: 1, Page: 013130-null
2014
- 71Citations
- 171Captures
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Metrics Details
- Citations71
- Citation Indexes70
- 70
- CrossRef61
- Policy Citations1
- Policy Citation1
- Captures171
- Readers171
- 171
Article Description
Opinion exchange models aim to describe the process of public opinion formation, seeking to uncover the intrinsic mechanism in social systems; however, the model results are seldom empirically justified using large-scale actual data. Online social media provide an abundance of data on opinion interaction, but the question of whether opinion models are suitable for characterizing opinion formation on social media still requires exploration. We collect a large amount of user interaction information from an actual social network, i.e., Twitter, and analyze the dynamic sentiments of users about different topics to investigate realistic opinion evolution. We find two nontrivial results from these data. First, public opinion often evolves to an ordered state in which one opinion predominates, but not to complete consensus. Second, agents are reluctant to change their opinions, and the distribution of the number of individual opinion changes follows a power law. Then, we suggest a model in which agents take external actions to express their internal opinions according to their activity. Conversely, individual actions can influence the activity and opinions of neighbors. The probability that an agent changes its opinion depends nonlinearly on the fraction of opponents who have taken an action. Simulation results show user action patterns and the evolution of public opinion in the model coincide with the empirical data. For different nonlinear parameters, the system may approach different regimes. A large decay in individual activity slows down the dynamics, but causes more ordering in the system.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=84921424797&origin=inward; http://dx.doi.org/10.1063/1.4866011; http://www.ncbi.nlm.nih.gov/pubmed/24697392; https://pubs.aip.org/cha/article/24/1/013130/135534/Opinion-formation-on-social-media-An-empirical; http://scitation.aip.org/content/aip/journal/chaos/24/1/10.1063/1.4866011; http://scitation.aip.org/limit_exceeded.html
AIP Publishing
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