What You Can Scrape and What Is Right to Scrape: A Proposal for a Tool to Collect Public Facebook Data
Open accessAbstract
In reaction to the Cambridge Analytica scandal, Facebook has restricted the access to its Application Programming Interface (API). This new policy has damaged the possibility for independent researchers to study relevant topics in political and social behavior. Yet, much of the public information that the researchers may be interested in is still available on Facebook, and can be still systematically collected through web scraping techniques. The goal of this article is twofold. First, we discuss some ethical and legal issues that researchers should consider as they plan their collection and possible publication of Facebook data. In particular, we discuss what kind of information can be ethically gathered about the users (public information), how published data should look like to comply with privacy regulations (like the GDPR), and what consequences violating Facebook’s terms of service may entail for the researcher. Second, we present a scraping routine for public Facebook posts, and discuss some technical adjustments that can be performed for the data to be ethically and legally acceptable. The code employs screen scraping to collect the list of reactions to a Facebook public post, and performs a one-way cryptographic hash function on the users’ identifiers to pseudonymize their personal information, while still keeping them traceable within the data. This article contributes to the debate around freedom of internet research and the ethical concerns that might arise by scraping data from the social web.
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Machine learning approaches enhance cyberbullying detection by extracting features such as textual instances, behavioral patterns, and demographics, thus improving precision.
“Machine learning approaches enhance cyberbullying detection by extracting features such as textual instances, behavioral patterns, and demographics, thus improving precision [73].”
From Cyberbullying detection: Exploring datasets, technologies, and approaches on social media platforms (2026) · cites this paper as [73]
Integrating textual, contextual, emotional, social attributes, and sentiment characteristics with conventional text features can elevate cyberbullying detection efficiency.
“Integrating textual, contextual, emotional, social attributes, and sentiment characteristics together with conventional text features can elevate cyberbullying”
From Cyberbullying detection: Exploring datasets, technologies, and approaches on social media platforms (2026) · cites this paper as [73]
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References 27 · Cited by 105
Research works printed but not linked: 4
- Beurskens, M. (2013). Legal questions of Twitter research. In K. Weller, A. Bruns, J. Burgess, M. Mahrt, & C. Puschmann (Eds.), Twitter and society (pp. 123-133).
- Hern, A. (2018). How to check whether Facebook shared your data with Cambridge Analytica. The Guardian. https://tinyurl.com/ ybvttn35 HiQ Labs v. LinkedIn. HiQ Labs v. LinkedIn Opinion., No.17- 16783 (Ninth Circuit Court of Appeals September 9, 2019).
- Kang, C., & Frenkel, S. (2018). Facebook says Cambridge Analytica harvested data of up to 87 million users. The New York Times. https://tinyurl.com/y85kfv2z
- In K. Weller, A. Bruns, J. Burgess, M. Mahrt, & C. Puschmann (Eds.), Twitter and society (pp. 43-54).
- Authors
- Mancosu, M., & Vegetti, F.
- Affiliations
- University of Turin (IT)
- University of Milan (IT)
- Corresponding author
- Mancosu, Moreno
- Venue
- Social Media + Society
- Publisher
- SAGE Publications
- Published
- 2020-07
- Volume
- 6
- Issue
- 3
- ISSN
- 2056-3051 (ISSN-L)
- OpenAlex
- W3046666383
- Cited by
- 105
- References
- 27
- Citations by year
- 2026: 11 · 2025: 33 · 2024: 14 · 2023: 20 · 2022: 14 · 2021: 12 · 2020: 1
- FWCI
- 15.71
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Social Media + Society (publishedVersion, cc-by-nc)
- Institutional Research Information System University of Turin (University of Turin) (submittedVersion, cc-by-nc)
- INFM-OAR (INFN Catania) (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Social Sciences › Sociology and Political Science › Privacy, Security, and Data Protection
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Physical Sciences › Computer Science › Information Systems › Web Data Mining and Analysis
- Keywords
- Facebook, web scraping, Cambridge Analytica scandal, research ethics, data privacy, GDPR, pseudonymization, cryptographic hashing, Facebook posts, user response
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