Journal of Computational Social Science

Aims & Scope

The Journal of Computational Social Science (JCSS) is an interdisciplinary peer-reviewed journal that ties together groundbreaking research across the strata of the social sciences (sociology, economics, political science, psychology, linguistics, and other disciplines), physics, biology, management science, computer science, and data science.

In addition to topics conventionally associated with computational social science, the journal invites contributions that analyze social/ economic phenomena or structures using computational approaches related to, but not restricted to, the following methods or fields: complex systems, economic modeling, econophysics, financial networks, risk management, urban planning, transportation analysis, artificial intelligence, image processing, text analytics, computational linguistics, numerical optimization, simulation-based statistical inference, and high-performance computing.

We invite contributions from researchers in any field who analyze social/ economic phenomena or structures based on large-scale data, simulations, or other computational approaches.

View Aims & Scope

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Metrics & Ranking

Impact Factor

Year	Value
2025	2.3
2024	2.00

Journal Rank

Year	Value
2024	9258

Journal Citation Indicator

Year	Value
2024	446

SJR (SCImago Journal Rank)

Year	Value
2024	0.625

Quartile

Year	Value
2024	Q2

h-index

Year	Value
2024	24

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Abstracting & Indexing

Journal is indexed in leading academic databases, ensuring global visibility and accessibility of our peer-reviewed research.

• Scopus
• Google Scholar
• Web of Science

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Subjects & Keywords

Journal’s research areas, covering key disciplines and specialized sub-topics in Computer Science and Social Sciences, designed to support cutting-edge academic discovery.

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Most Cited Articles

The Most Cited Articles section features the journal's most impactful research, based on citation counts. These articles have been referenced frequently by other researchers, indicating their significant contribution to their respective fields.

• Conspiracy in the time of corona: automatic detection of emerging COVID-19 conspiracy theories in social media and the news

  Citation: 191

  Authors: Shadi, Pavan, Tianyi, Timothy R., Vwani

• Social influence and unfollowing accelerate the emergence of echo chambers

  Citation: 140

  Authors: Kazutoshi, Wen, Hao, Giovanni Luca, Alessandro, Filippo

• Misinformation, manipulation, and abuse on social media in the era of COVID-19

  Citation: 138

  Authors: Emilio, Stefano, Luca

• Network models of financial systemic risk: a review

  Citation: 124

  Authors: Fabio, Paolo, Teruyoshi

• Enhanced news sentiment analysis using deep learning methods

  Citation: 107

  Authors: Wataru, Irena, Hideaki

• GenAI against humanity: nefarious applications of generative artificial intelligence and large language models

  Citation: 103

  Authors: Emilio

• Botometer 101: social bot practicum for computational social scientists

  Citation: 94

  Authors: Kai-Cheng, Emilio, Filippo

• Partisan public health: how does political ideology influence support for COVID-19 related misinformation?

  Citation: 92

  Authors: Nicholas Francis

• Digital media and misinformation: An outlook on multidisciplinary strategies against manipulation

  Citation: 82

  Authors: Danielle, Mário J.

• Bots and online hate during the COVID-19 pandemic: case studies in the United States and the Philippines

  Citation: 73

  Authors: Joshua, Kathleen M.

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