Analytic tools are beginning to be largely employed,given their ability to rank, e.g., the visibility of social mediausers. Visibility that, in turns, can have a monetary value, sincesocial media popular people usually either anticipate or establishtrends that could impact the real world (at least, from a consumerpoint of view). The above rationale has fostered the flourishingof private companies providing statistical results for social mediaanalysis. These results have been accepted, and largely diffused,by media without any apparent scrutiny, while Academia hasmoderately focused its attention on this phenomenon.In this paper, we provide evidence that analytic resultsprovided by field-flagship companies are questionable (at least).In particular, we focus on Twitter and its "fake followers". Wesurvey popular Twitter analytics that count the fake followers ofsome target account. We perform a series of experiments aimedat verifying the trustworthiness of their results. We compare theresults of such tools with a machine-learning classifier whosemethodology bases on scientific basis and on a sound samplingscheme. The findings of this work call for a serious re-thinking ofthe methodology currently used by companies providing analyticresults, whose present deliveries seem to lack on any reliability

A Criticism to Society (as seen by Twitter analytics)

Stefano Cresci;Marinella Petrocchi;Angelo Spognardi;Maurizio Tesconi
2014

Abstract

Analytic tools are beginning to be largely employed,given their ability to rank, e.g., the visibility of social mediausers. Visibility that, in turns, can have a monetary value, sincesocial media popular people usually either anticipate or establishtrends that could impact the real world (at least, from a consumerpoint of view). The above rationale has fostered the flourishingof private companies providing statistical results for social mediaanalysis. These results have been accepted, and largely diffused,by media without any apparent scrutiny, while Academia hasmoderately focused its attention on this phenomenon.In this paper, we provide evidence that analytic resultsprovided by field-flagship companies are questionable (at least).In particular, we focus on Twitter and its "fake followers". Wesurvey popular Twitter analytics that count the fake followers ofsome target account. We perform a series of experiments aimedat verifying the trustworthiness of their results. We compare theresults of such tools with a machine-learning classifier whosemethodology bases on scientific basis and on a sound samplingscheme. The findings of this work call for a serious re-thinking ofthe methodology currently used by companies providing analyticresults, whose present deliveries seem to lack on any reliability
2014
Istituto di informatica e telematica - IIT
Fake followers analysis
Fake followers Lightweight Classifier
machine learning
Twitter analytics
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.14243/255567
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus ND
  • ???jsp.display-item.citation.isi??? ND
social impact