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  Distilling Information Reliability and Source Trustworthiness from Digital Traces

Tabibian, B., Valera, I., Farajtabar, M., Song, L., Schölkopf, B., & Gomez Rodriguez, M. (2016). Distilling Information Reliability and Source Trustworthiness from Digital Traces. doi:10.1145/3038912.3052672.

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arXiv:1610.07472.pdf (Preprint), 804KB
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arXiv:1610.07472.pdf
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File downloaded from arXiv at 2017-04-13 11:12 Accepted at 26th World Wide Web conference (WWW-17)
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 Urheber:
Tabibian, Behzad1, Autor
Valera, Isabel2, Autor           
Farajtabar, Mehrdad1, Autor
Song, Le1, Autor
Schölkopf, Bernhard1, Autor
Gomez Rodriguez, Manuel2, Autor           
Affiliations:
1External Organizations, ou_persistent22              
2Group M. Gomez Rodriguez, Max Planck Institute for Software Systems, Max Planck Society, ou_2105290              

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Schlagwörter: cs.SI,Statistics, Machine Learning, stat.ML
 Zusammenfassung: Online knowledge repositories typically rely on their users or dedicated editors to evaluate the reliability of their content. These evaluations can be viewed as noisy measurements of both information reliability and information source trustworthiness. Can we leverage these noisy evaluations, often biased, to distill a robust, unbiased and interpretable measure of both notions? In this paper, we argue that the temporal traces left by these noisy evaluations give cues on the reliability of the information and the trustworthiness of the sources. Then, we propose a temporal point process modeling framework that links these temporal traces to robust, unbiased and interpretable notions of information reliability and source trustworthiness. Furthermore, we develop an efficient convex optimization procedure to learn the parameters of the model from historical traces. Experiments on real-world data gathered from Wikipedia and Stack Overflow show that our modeling framework accurately predicts evaluation events, provides an interpretable measure of information reliability and source trustworthiness, and yields interesting insights about real-world events.

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Sprache(n): eng - English
 Datum: 2016-10-242017-04-022016
 Publikationsstatus: Online veröffentlicht
 Seiten: 15 p.
 Ort, Verlag, Ausgabe: -
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 Identifikatoren: arXiv: 1610.07472
DOI: 10.1145/3038912.3052672
URI: http://arxiv.org/abs/1610.07472
BibTex Citekey: Tabibian2016
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