liu.seSearch for publications in DiVA
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Social Media Dynamics of Shorted Companies
Linköping University, Department of Computer and Information Science, Database and information techniques. Linköping University, Faculty of Science & Engineering.
Linköping University, Department of Computer and Information Science, Database and information techniques. Linköping University, Faculty of Science & Engineering.
Linköping University, Department of Computer and Information Science, Database and information techniques. Linköping University, Faculty of Science & Engineering.
Linköping University, Department of Computer and Information Science, Database and information techniques. Linköping University, Faculty of Science & Engineering.ORCID iD: 0000-0003-1367-1594
2022 (English)In: Proc. International Conference on Social Networks Analysis, Management and Security (SNAMS), Institute of Electrical and Electronics Engineers (IEEE), 2022Conference paper, Published paper (Refereed)
Abstract [en]

The discussions on social-media forums can impact the sentiment of a company, and consequently also its stock price. As we show here, some of the most shorted companies have provided some of the clearest examples of this relationship. In light of these observations, this paper presents a longitudinal study of the cross-forum dynamics of ten highly shorted stocks that saw significant discussions on the popular forums Reddit, Twitter, and Seeking Alpha. Using the posts from these forums, their sentiments, and the daily snapshots of the stock price of each company, we use a combination of qualitative case studies and quantitative hypothesis testing to derive new insights. Through a combination of time-series analysis, clustering, and domain-optimized sentiment analysis, we study the relationship between the times that discussions peak on the different forums, the changes in sentiment, and the stock price movements. We find that all three forums are likely to experience peaks in their activity close to each other, that Reddit is most likely to peak first, and that the sentiment of Twitter discussions were more sensitive to the current derivative of the stock price than the sentiment observed on the other forums.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2022.
Series
2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), ISSN 2831-7351, E-ISSN 2831-7343
Keywords [en]
Longitudinal analysis; Sentiment analysis; Shorted companies; Social media dynamics
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:liu:diva-199088DOI: 10.1109/SNAMS58071.2022.10062796Scopus ID: 2-s2.0-85158942321ISBN: 9798350320480 (electronic)ISBN: 9798350320497 (print)OAI: oai:DiVA.org:liu-199088DiVA, id: diva2:1811231
Conference
International Conference on Social Networks Analysis, Management and Security (SNAMS), Milan, Italy, 29 November - 01 December, 2022.
Available from: 2023-11-11 Created: 2023-11-11 Last updated: 2023-11-15Bibliographically approved

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full textScopus

Authority records

Terve, CarlMohammadinodooshan, AlirezaCarlsson, Niklas

Search in DiVA

By author/editor
Terve, CarlErlingsson, MattiasMohammadinodooshan, AlirezaCarlsson, Niklas
By organisation
Database and information techniquesFaculty of Science & Engineering
Computer Sciences

Search outside of DiVA

GoogleGoogle Scholar

doi
isbn
urn-nbn

Altmetric score

doi
isbn
urn-nbn
Total: 44 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf