A Criticism to Society (As Seen by Twitter Analytics)
Abstract
Analytic tools are beginning to be largely employed, given their ability to rank, e.g., the visibility of social media users. Visibility that, in turns, can have a monetary value, since social media popular people usually either anticipate or establish trends that could impact the real world (at least, from a consumer point of view). The above rationale has fostered the flourishing of private companies providing statistical results for social media analysis. These results have been accepted, and largely diffused, by media without any apparent scrutiny, while Academia has moderately focused its attention on this phenomenon. In this paper, we provide evidence that analytic results provided by field-flagship companies are questionable (at least). In particular, we focus on Twitter and its "fake followers". We survey popular Twitter analytics that count the fake followers of some target account. We perform a series of experiments aimed at verifying the trustworthiness of their results. We compare the results of such tools with a machine-learning classifier whose methodology bases on scientific basis and on a sound sampling scheme. The findings of this work call for a serious re-thinking of the methodology currently used by companies providing analytic results, whose present deliveries seem to lack on any reliability.
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These analyses are affected by several biases like small and statistically unsound sampling strategies.
“Moreover, as demonstrated in our previous work [16], these analyses are affected by several biases like small and statistically unsound sampling strategies.”
From Fame for sale: Efficient detection of fake Twitter followers (2015) · cites this paper as [16]
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References 7 · Cited by 20
- Authors
- Cresci, S., Petrocchi, M., Spognardi, A., Tesconi, M., & Pietro, R. D.
- Affiliations
- Institute of Informatics and Telematics (IT)
- Venue
- 2014 IEEE 34th International Conference on Distributed Computing Systems Workshops
- Publisher
- IEEE
- Published
- 2014-06
- Pages
- 194-200
- OpenAlex
- W2165985862
- Cited by
- 20
- References
- 7
- Citations by year
- 2024: 2 · 2021: 1 · 2019: 3 · 2018: 1 · 2017: 6 · 2016: 4 · 2015: 2 · 2014: 1
- FWCI
- 13.95
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE International Conference on Distributed Computing Systems Workshops (ICDCSW) (publishedVersion)
- CINECA IRIS Institutial research information system (University of Pisa) (submittedVersion)
- IRIS Research product catalog (Sapienza University of Rome) (submittedVersion)
- Topics
- Social Sciences › Sociology and Political Science › Misinformation and Its Impacts
- Physical Sciences › Computer Science › Information Systems › Spam and Phishing Detection
- Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Complex Network Analysis Techniques
- Keywords
- Twitter analytics, fake followers, social media visibility, social media companies, machine learning classifier comparison, sampling methods, social media influence
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