Cited by 20Misinformation and Its ImpactsSpam and Phishing DetectionComplex Network Analysis Techniques

A Criticism to Society (As Seen by Twitter Analytics)

Cresci, Stefano, Petrocchi, Marinella, Spognardi, Angelo, Tesconi, Maurizio, Pietro, Roberto Di·2014·2014 IEEE 34th International Conference on Distributed Computing Systems Workshops

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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Crossref · 10.1109/icdcsw.2014.31

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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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