Who framed Roger Reindeer? De-censorship of Facebook posts by snippet classification
| Autor: | Marinella Petrocchi, Maurizio Tesconi, Fabio Del Vigna, Cesare Zavattari, Alessandro Tommasi |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: |
FOS: Computer and information sciences
Computer Networks and Communications Computer science media_common.quotation_subject Internet privacy News censorship Identities censorship Text mining Data correlation Privacy in social networks Social media mining Facebook Named entity recognition Context (language use) ComputingMilieux_LEGALASPECTSOFCOMPUTING 02 engineering and technology 010501 environmental sciences 01 natural sciences 020204 information systems Phenomenon 0202 electrical engineering electronic engineering information engineering Adaptation (computer science) 0105 earth and related environmental sciences media_common Computer Science - Computation and Language Social network business.industry Communication Censorship Subject (documents) Snippet business Computation and Language (cs.CL) Personally identifiable information Information Systems |
| Zdroj: | Online social networks and media (2018): 41–57. info:cnr-pdr/source/autori:Fabio Del Vigna, Marinella Petrocchi, Alessandro Tommasi, Cesare Zavattari, Maurizio Tesconi/titolo:Who framed Roger Reindeer? De-censorship of Facebook posts by snippet classification/doi:/rivista:Online social networks and media/anno:2018/pagina_da:41/pagina_a:57/intervallo_pagine:41–57/volume |
| Popis: | This paper considers online news censorship and it concentrates on censorship of identities. Obfuscating identities may occur for disparate reasons, from military to judiciary ones. In the majority of cases, this happens to protect individuals from being identified and persecuted by hostile people. However, being the collaborative web characterised by a redundancy of information, it is not unusual that the same fact is reported by multiple sources, which may not apply the same restriction policies in terms of censorship. Also, the proven aptitude of social network users to disclose personal information leads to the phenomenon that comments to news can reveal the data withheld in the news itself. This gives us a mean to figure out who the subject of the censored news is. We propose an adaptation of a text analysis approach to unveil censored identities. The approach is tested on a synthesised scenario, which however resembles a real use case. Leveraging a text analysis based on a context classifier trained over snippets from posts and comments of Facebook pages, we achieve promising results. Despite the quite constrained settings in which we operate -- such as considering only snippets of very short length -- our system successfully detects the censored name, choosing among 10 different candidate names, in more than 50\% of the investigated cases. This outperforms the results of two reference baselines. The findings reported in this paper, other than being supported by a thorough experimental methodology and interesting on their own, also pave the way for further investigation on the insidious issues of censorship on the web. Comment: Accepted for publication: Elsevier Online Social Networks and Media |
| Databáze: | OpenAIRE |
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