Conversationally-inspired stylometric features for authorship attribution in instant messaging

Authors:
Marco Cristani;Giorgio Roffo;Cristina Segalin;Loris Bazzani;Alessandro Vinciarelli;Vittorio Murino
Affiliations:
Università degli Studi di Verona, Verona, Italy;Istituto Italiano di Tecnologia, Genova, Italy;Istituto Italiano di Tecnologia, Genova, Italy;Istituto Italiano di Tecnologia, Genova, Italy;University of Glasgow, Glasgow, Scotland Uk;Istituto Italiano di Tecnologia, Genova, Italy
Venue:
Proceedings of the 20th ACM international conference on Multimedia
Year:
2012

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Abstract

Authorship attribution (AA) aims at recognizing automatically the author of a given text sample. Traditionally applied to literary texts, AA faces now the new challenge of recognizing the identity of people involved in chat conversations. These share many aspects with spoken conversations, but AA approaches did not take it into account so far. Hence, this paper tries to fill the gap and proposes two novelties that improve the effectiveness of traditional AA approaches for this type of data: the first is to adopt features inspired by Conversation Analysis (in particular for turn-taking), the second is to extract the features from individual turns rather than from entire conversations. The experiments have been performed over a corpus of dyadic chat conversations (77 individuals in total). The performance in identifying the persons involved in each exchange, measured in terms of area under the Cumulative Match Characteristic curve, is 89.5%.