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Tampering with Twitter’s Sample API
Open accessAbstract
Social media data is widely analyzed in computational social science. Twitter, one of the largest social media platforms, is used for research, journalism, business, and government to analyze human behavior at scale. Twitter offers data via three different Application Programming Interfaces (APIs). One of which, Twitter’s Sample API, provides a freely available 1% and a costly 10% sample of all Tweets. These data are supposedly random samples of all platform activity. However, we demonstrate that, due to the nature of Twitter’s sampling mechanism, it is possible to deliberately influence these samples, the extent and content of any topic, and consequently to manipulate the analyses of researchers, journalists, as well as market and political analysts trusting these data sources. Our analysis also reveals that technical artifacts can accidentally skew Twitter’s samples. Samples should therefore not be regarded as random. Our findings illustrate the critical limitations and general issues of big data sampling, especially in the context of proprietary data and undisclosed details about data handling.
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“Such accounts use tricks such as hijacking “trending” hashtags of keywords to gain visibility (Thomas et al., 2011), and can (purposefully or accidentaly) distort the statistics of datasets collected from social platforms”
From Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries (2019) · cites this paper as Pfeffer et al., 2018
In the citing paper
“However, the Twitter Streaming API is like a black-box with a lack of transparency in the sampling methodology [41,42], yet this is one of the few forms of unfettered and large-scale social media data access to researchers outside social media companies [43] and has been used in prior research, including in health-related studies [35,44,45].”
From Psychosocial Effects of the COVID-19 Pandemic: Large-scale Quasi-Experimental Study on Social Media (2020) · cites this paper as [41]
In the citing paper
“Recent research has also questioned the credibility of the “1% Twitter stream” aspect, noting that actual sampling data was smaller than what it ideally should have been [41].”
From Psychosocial Effects of the COVID-19 Pandemic: Large-scale Quasi-Experimental Study on Social Media (2020) · cites this paper as [41]
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Cites
A Mathematical Theory of Communication
Shannon, C. E. (1948) · Bell System Technical Journal · doi:10.1002/j.1538-7305.1948.tb01338.x · Cited by 88,486
Opinion Mining and Sentiment Analysis
Pang, B., & Lee, L. (2008) · now publishers, Inc. eBooks · doi:10.1561/9781601981516 · Cited by 6,957
Opinion mining and sentiment analysis
Pang, B., & Lee, L. (2008) · Foundations and Trends® in Information Retrieval · doi:10.1561/1500000011 · Cited by 6,372
VADER: a parsimonious rule-based model for sentiment analysis of social media text
Hutto, C., & Gilbert, E. (2014) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v8i1.14550 · Cited by 6,087
Big data: a revolution that will transform how we live, work, and think
(2013) · Choice Reviews Online · doi:10.5860/choice.50-6804 · Cited by 3,657
Earthquake shakes Twitter users
Sakaki, T., Okazaki, M., & Matsuo, Y. (2010) · International World Wide Web Conference (WWW) · doi:10.1145/1772690.1772777 · Cited by 3,631
Lazer, D., Pentland, A., Adamic, L., Aral, S., Barabási, A.-L., Brewer, D., Christakis, N., Contractor, N., Fowler, J., Gutmann, M., et al. (2009) · Science · doi:10.1126/science.1167742 · Cited by 3,365
Twitter as a Corpus for Sentiment Analysis and Opinion Mining
Pak, A., & Paroubek, P. (2010) · Language Resources and Evaluation Conference (LREC) · doi:10.63317/47akcj8foo4d · Cited by 2,784
Predicting Elections with Twitter: What 140 Characters Reveal about Political Sentiment
Tumasjan, A., Sprenger, T. O., Sandner, P., & Welpe, I. M. (2010) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v4i1.14009 · Cited by 2,647
The Parable of Google Flu: Traps in Big Data Analysis
Lazer, D., Kennedy, R., King, G., & Vespignani, A. (2014) · Science · doi:10.1126/science.1248506 · Cited by 2,523
Research works printed but not linked: 6
- Elmer G, Langlois G, Redden J (2015) Compromised data: from social media to big data. Bloomsbury Publishing, New York
- Gaffney D, Puschmann C (2013) Data collection on Twitter, pp. 55–67. Peter Lang, New York
- Neuberger C, vom Hofe J, Nuernbergk C (2014) The use of Twitter by professional journalists. results of a newsroom survey in Germany. In: Weller K, Bruns A, Burgess J, Mahrt M, Puschmann C (eds) Twitter and society. Peter Lang, New York, pp 345–357
- Elmer G, Langlois G, Redden J (2015) Compromised data: from social media to big data. Bloomsbury Publishing, USA
- Weller K, Bruns A, Burgess J, Mahrt M, Puschmann C Twitter and Society, vol. 89. P. Lang
- de Saint-Exupéry A (1943) The Little Prince. Reynal & Hitchcock, New York
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“, 2011), and can (purposefully or accidentaly) distort the statistics of datasets collected from social platforms (Morstatter et al., 2016; Pfeffer et al., 2018).”
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Multimodal approach to analysing big social and news media data
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Pfeffer, J., Voggenreiter, A., Lasser, J., Hammer, L., Stritzel, O., & García, D. (2023) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v17i1.22182 · Cited by 66
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Radicioni, T., Saracco, F., Pavan, E., & Squartini, T. (2021) · Scientific Reports · doi:10.1038/s41598-021-92337-2 · Cited by 40
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Pérez, J. M., Furman, D., Alemany, L. A., & Luque, F. M. (2022) · Language Resources and Evaluation Conference (LREC) · doi:10.63317/4v9eufgmyo6q · Cited by 97
Just Another Day on Twitter: A Complete 24 Hours of Twitter Data
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Barron, A. T. J., & Bollen, J. (2022) · Scientific Reports · doi:10.1038/s41598-022-19181-w · Cited by 34
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Metadata
- Authors
- Pfeffer, J., Mayer, K., & Morstatter, F.
- Affiliations
- Technical University of Munich (DE)
- University of Southern California (US)
- Corresponding author
- Pfeffer, Jürgen
- Venue
- EPJ Data Science
- Published
- 2018-12
- Volume
- 7
- Issue
- 1
- ISSN
- 2193-1127 (ISSN-L)
- OpenAlex
- W2904605268
- Cited by
- 135
- References
- 82
- Citations by year
- 2026: 8 · 2025: 10 · 2024: 7 · 2023: 22 · 2022: 22 · 2021: 25 · 2020: 31 · 2019: 10
- FWCI
- 23.42
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- EPJ Data Science (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- mediaTUM – the media and publications repository of the Technical University Munich (Technical University Munich) (submittedVersion, other-oa)
- Funders
- Deutsche Forschungsgemeinschaft
- Technische Universität München
- Topics
- Social Sciences › Decision Sciences › Information Systems and Management › Big Data Technologies and Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Ethics and Social Impacts of AI
- Social Sciences › General Social Sciences › Computational and Text Analysis Methods
- Keywords
- sampling bias, tweets, data manipulation, random sampling, technical artifacts, proprietary data, social media data
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