Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
Editorial
. 2024 Feb 29:10:e1909.
doi: 10.7717/peerj-cs.1909. eCollection 2024.

Special issue on analysis and mining of social media data

Affiliations
Editorial

Special issue on analysis and mining of social media data

Arkaitz Zubiaga et al. PeerJ Comput Sci. .

Abstract

This Editorial introduces the PeerJ Computer Science Special Issue on Analysis and Mining of Social Media Data. The special issue called for submissions with a primary focus on the use of social media data, for a variety of fields including natural language processing, computational social science, data mining, information retrieval and recommender systems. Of the 48 abstract submissions that were deemed within the scope of the special issue and were invited to submit a full article, 17 were ultimately accepted. These included a diverse set of articles covering, inter alia, sentiment analysis, detection and mitigation of online harms, analytical studies focused on societal issues and analysis of images surrounding news. The articles primarily use Twitter, Facebook and Reddit as data sources; English, Arabic, Italian, Russian, Indonesian and Javanese as languages; and over a third of the articles revolve around COVID-19 as the main topic of study. This article discusses the motivation for launching such a special issue and provides an overview of the articles published in the issue.

Keywords: Computational social science; Data mining; Natural language processing; Social media.

PubMed Disclaimer

Conflict of interest statement

Arkaitz Zubiaga and Paolo Rosso are Academic Editors for PeerJ Computer Science.

Similar articles

References

    1. Al-nuwaiser WM. Effect of visual imagery in COVID-19 social media posts on users’ perception. PeerJ Computer Science. 2022;8:e1153. doi: 10.7717/peerj-cs.1153. - DOI - PMC - PubMed
    1. Al Tamime R, Weber I. Using social media advertisement data to monitor the gender gap in STEM: opportunities and challenges. PeerJ Computer Science. 2022;8:e994. doi: 10.7717/peerj-cs.994. - DOI - PMC - PubMed
    1. Ali MF, Irfan R, Lashari TA. Comprehensive sentimental analysis of tweets towards COVID-19 in Pakistan: a study on governmental preventive measures. PeerJ Computer Science. 2023;9:e1220. doi: 10.7717/peerj-cs.1220. - DOI - PMC - PubMed
    1. Almerekhi H, Kwak H, Jansen BJ. Investigating toxicity changes of cross-community redditors from 2 billion posts and comments. PeerJ Computer Science. 2022;8:e1059. doi: 10.7717/peerj-cs.1059. - DOI - PMC - PubMed
    1. Baghdadi NA, Malki A, Magdy Balaha H, AbdulAzeem Y, Badawy M, Elhosseini M. An optimized deep learning approach for suicide detection through Arabic tweets. PeerJ Computer Science. 2022;8:e1070. doi: 10.7717/peerj-cs.1070. - DOI - PMC - PubMed

Publication types