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Advancing the Use of Information Compression Distances in Authorship Attribution

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Disinformation in Open Online Media (MISDOOM 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13545 ))

Abstract

Detecting unreliable information in social media is an open challenge, in part as a result of the difficulty to associate a piece of information to known and trustworthy actors. The identification of the origin of sources can help society deal with unverified, incomplete, or even false information. In this work we tackle the problem of associating a piece of information to a certain politician. The use of inaccurate information is of great relevance in the case of politicians, since it affects social perception and voting behavior. Moreover, misquotation can be weaponized to hinder adversary reputation. We consider the task of applying a compression-based metric to conduct authorship attribution in social media, namely in Twitter. In specific, we leverage the Normalized Compression Distance (NCD) to compare an author’s text with other authors’ texts. We show that this methodology performs well, obtaining 80.3% accuracy in a scenario with 6 different politicians.

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Notes

  1. 1.

    The original dataset can be downloaded from https://www.reddit.com/r/datasets/comments/6fniik/over_one_million_tweets_collected_from_us/.

  2. 2.

    For more information visit https://datatracker.ietf.org/doc/html/rfc1951.

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Acknowledgements

This project has received funding from the European Union’s Horizon 2020 Research and Innovation Programme under grant agreement No. 872855 (TRESCA project), from Grant PLEC2021-007681 (project XAI-DisInfodemics) funded by MCIN/AEI/ 10.13039/501100011033 and by European Union NextGeneration EU/PRTR, from Comunidad de Madrid (Spain) under the project CYNAMON (no. P2018/TCS-4566), cofunded with FSE and FEDER EU funds, and from Spanish projects MINECO/FEDER TIN2017-84452-R and PID2020-114867RB-I00 (http://www.mineco.gob.es/).

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Authors and Affiliations

  1. Institute for Physical and Information Technologies “Leonardo Torres Quevedo” (ITEFI), Consejo Superior de Investigaciones Científicas (CSIC), Madrid, Spain

    Santiago Palmero Muñoz & David Arroyo

  2. Universidad Autónoma de Madrid, 28049, Madrid, Spain

    Christian Oliva & Luis F. Lago-Fernández

Authors
  1. Santiago Palmero Muñoz
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  2. Christian Oliva
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  3. Luis F. Lago-Fernández
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  4. David Arroyo
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Corresponding author

Correspondence to Christian Oliva .

Editor information

Editors and Affiliations

  1. Boise State University, Boise, ID, USA

    Francesca Spezzano

  2. Universidade do Vale do Rio dos Sinos, São Leopoldo, Brazil

    Adriana Amaral

  3. Centrum Wiskunde and Informatica, Amsterdam, The Netherlands

    Davide Ceolin

  4. Vanderbilt University, Nashville, TN, USA

    Lisa Fazio

  5. Boise State University, Boise, ID, USA

    Edoardo Serra

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Muñoz, S.P., Oliva, C., Lago-Fernández, L.F., Arroyo, D. (2022). Advancing the Use of Information Compression Distances in Authorship Attribution. In: Spezzano, F., Amaral, A., Ceolin, D., Fazio, L., Serra, E. (eds) Disinformation in Open Online Media. MISDOOM 2022. Lecture Notes in Computer Science, vol 13545 . Springer, Cham. https://doi.org/10.1007/978-3-031-18253-2_8

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  • DOI: https://doi.org/10.1007/978-3-031-18253-2_8

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