Controversial information spreads faster and further than non-controversial information in Reddit
Jasser Jasser (),
Ivan Garibay,
Steve Scheinert and
Alexander V. Mantzaris
Additional contact information
Jasser Jasser: University of Central Florida
Steve Scheinert: LLC
Alexander V. Mantzaris: University of Central Florida
Journal of Computational Social Science, 2022, vol. 5, issue 1, No 6, 122 pages
Abstract:
Abstract Online users discuss and converse about all sorts of topics on social networks. Facebook, Twitter, Reddit are among many other networks where users can have this freedom of information sharing. The abundance of information shared over these networks makes them an attractive area for investigating all aspects of human behavior on information dissemination. Among the many interesting behaviors, controversiality within social cascades is of high interest to us. It is known that controversiality is bound to happen within online discussions. The online social network platform Reddit has the feature to tag comments as controversial if the users have mixed opinions about that comment. The difference between this study and previous attempts at understanding controversiality on social networks is that we do not investigate topics that are known to be controversial. On the contrary, we examine typical cascades with comments that the readers deemed to be controversial concerning the matter discussed. This work asks whether controversially initiated information cascades have distinctive characteristics than those not controversial in Reddit. We used data collected from Reddit consisting of around 17 million posts and their corresponding comments related to cybersecurity issues to answer these emerging questions. From the comparative analyses conducted, controversial content travels faster and further from its origin. Understanding this phenomenon would shed light on how users or organization might use it to their help in controlling and spreading a specific beneficiary message.
Keywords: Controversiality; Information; Diffusion; Reddit; Polarization (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)
Downloads: (external link)
http://link.springer.com/10.1007/s42001-021-00121-z Abstract (text/html)
Access to the full text of the articles in this series is restricted.
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:spr:jcsosc:v:5:y:2022:i:1:d:10.1007_s42001-021-00121-z
Ordering information: This journal article can be ordered from
http://www.springer. ... iences/journal/42001
DOI: 10.1007/s42001-021-00121-z
Access Statistics for this article
Journal of Computational Social Science is currently edited by Takashi Kamihigashi
More articles in Journal of Computational Social Science from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().