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Authorship Analysis of Social Media Contents Using Tone and Personality Features

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Security, Privacy, and Anonymity in Computation, Communication, and Storage (SpaCCS 2017)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10656))

Abstract

Online social networks have contributed to the countless services that ease human interaction. But the veil of anonymity has become a resort to majority of cyber criminals who indulge in unethical cyber activities. The availability of Wi-Fi hotspots and smart phones has made tracking the individuals behind the activities, a daunting task. To curtail the worst impact of these activities, one can make use of identifying authors of text contents in online social media, the only readily available imprint of an individual. Here we propose a novel authorship analysis technique applied on twitter data using tone based, personality based and stylistic features. We propose an authorship attribution scheme by training author data using Convolutional Neural Network pretrained on personality data and combines the features obtained from this model with the features obtained from another CNN architecture for tone analysis proposed by us. These features are combined together with hand crafted features pertaining to the stylistic aspects of the author and an SVM is trained on these feature combination. To the best of our knowledge this is the first work employing tone based and personality based features for attributing authorship. The new approach paves way for a fool proof authorship analysis mechanism that can be employed to curb security issues like hacked account. This is because the features chosen for our attribution method are difficult to be imitated as well as consciously controlled.

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Notes

  1. 1.

    http://cs229.stanford.edu/proj2012/CastroLindauerAuthorIdentificationOnTwitter.pdf.

  2. 2.

    http://www.sananalytics.com/lab/twitter-sentiment/.

  3. 3.

    https://www.crowdflower.com/wp-content/uploads/2016/07/text_emotion.csv.

  4. 4.

    http://emotion-research.net/toolbox/toolboxdatabase.2006-10-13.2581092615.

  5. 5.

    https://github.com/CrowdTruth/Short-Text-Corpus-For-Humor-Detection.

  6. 6.

    http://www.parrotanalytics.com/pacific-asia-knowledge-discovery-and-data-mining-conference-2016-contest/.

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Author information

Authors and Affiliations

  1. Indian Institute of Information Technology and Management Kerala, Trivandrum, India

    Athira Usha & Sabu M. Thampi

  2. CSE, Faculty of Engineering and Technology, University of Kerala, Trivandrum, India

    Athira Usha

Authors
  1. Athira Usha
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  2. Sabu M. Thampi
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Corresponding author

Correspondence to Athira Usha .

Editor information

Editors and Affiliations

  1. Guangzhou University , Guangzhou, China

    Guojun Wang

  2. Edith Kinney Gaylord Presidential Professor, University of Oklahoma, Norman, Oklahoma, USA

    Mohammed Atiquzzaman

  3. Aalto University, Espoo, Finland

    Zheng Yan

  4. University of Texas at San Antonio, San Antonio, Texas, USA

    Kim-Kwang Raymond Choo

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Usha, A., Thampi, S.M. (2017). Authorship Analysis of Social Media Contents Using Tone and Personality Features. In: Wang, G., Atiquzzaman, M., Yan, Z., Choo, KK. (eds) Security, Privacy, and Anonymity in Computation, Communication, and Storage. SpaCCS 2017. Lecture Notes in Computer Science(), vol 10656. Springer, Cham. https://doi.org/10.1007/978-3-319-72389-1_18

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  • DOI: https://doi.org/10.1007/978-3-319-72389-1_18

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-72388-4

  • Online ISBN: 978-3-319-72389-1

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