Of bots and humans (on twitter)

Zafar Gilani; Reza Farahbakhsh; Gareth Tyson; Liang Wang; Jon Crowcroft

doi:10.1145/3110025.3110090

Of bots and humans (on twitter)

Zafar Gilani, Reza Farahbakhsh, Gareth Tyson, Liang Wang, Jon Crowcroft

Research output: Chapter in Book/Conference Proceeding/Report › Conference Paper published in a book › peer-review

100 Citations (Scopus)

Abstract

Recent research has shown a substantial active presence of bots in online social networks (OSNs). In this paper we utilise our previous work (Stweeler) to comparatively analyse the usage and impact of bots and humans on Twitter, one of the largest OSNs in the world. We collect a large-scale Twitter dataset and define various metrics based on tweet metadata. Using a human annotation task we assign ‘bot’ and ‘human’ ground-truth labels to the dataset, and compare the annotations against an online bot detection tool for evaluation. We then ask a series of questions to discern important behavioural characteristics of bots and humans using metrics within and among four popularity groups. From the comparative analysis we draw differences and interesting similarities between the two entities, thus paving the way for reliable classification of bots, and studying automated political infiltration and advertisement campaigns.

Original language	English
Title of host publication	Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2017
Editors	Jana Diesner, Elena Ferrari, Guandong Xu
Publisher	Association for Computing Machinery, Inc
Pages	349-354
Number of pages	6
ISBN (Electronic)	9781450349932
DOIs	https://doi.org/10.1145/3110025.3110090
Publication status	Published - 31 Jul 2017
Externally published	Yes
Event	9th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2017 - Sydney, Australia Duration: 31 Jul 2017 → 3 Aug 2017

Publication series

Name	Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2017

Conference

Conference	9th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2017
Country/Territory	Australia
City	Sydney
Period	31/07/17 → 3/08/17

Bibliographical note

Publisher Copyright:
© 2017 Association for Computing Machinery.

Keywords

Behavioural analysis
Bot characterisation
Social network analysis
content propagation

Access to Document

10.1145/3110025.3110090

Cite this

Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., & Crowcroft, J. (2017). Of bots and humans (on twitter). In J. Diesner, E. Ferrari, & G. Xu (Eds.), Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2017 (pp. 349-354). (Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2017). Association for Computing Machinery, Inc. https://doi.org/10.1145/3110025.3110090

Gilani, Zafar ; Farahbakhsh, Reza ; Tyson, Gareth et al. / Of bots and humans (on twitter). Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2017. editor / Jana Diesner ; Elena Ferrari ; Guandong Xu. Association for Computing Machinery, Inc, 2017. pp. 349-354 (Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2017).

@inproceedings{9b38f64b5f874540a9e539ed85fb056c,

title = "Of bots and humans (on twitter)",

abstract = "Recent research has shown a substantial active presence of bots in online social networks (OSNs). In this paper we utilise our previous work (Stweeler) to comparatively analyse the usage and impact of bots and humans on Twitter, one of the largest OSNs in the world. We collect a large-scale Twitter dataset and define various metrics based on tweet metadata. Using a human annotation task we assign {\textquoteleft}bot{\textquoteright} and {\textquoteleft}human{\textquoteright} ground-truth labels to the dataset, and compare the annotations against an online bot detection tool for evaluation. We then ask a series of questions to discern important behavioural characteristics of bots and humans using metrics within and among four popularity groups. From the comparative analysis we draw differences and interesting similarities between the two entities, thus paving the way for reliable classification of bots, and studying automated political infiltration and advertisement campaigns.",

keywords = "Behavioural analysis, Bot characterisation, Social network analysis, content propagation",

author = "Zafar Gilani and Reza Farahbakhsh and Gareth Tyson and Liang Wang and Jon Crowcroft",

note = "Publisher Copyright: {\textcopyright} 2017 Association for Computing Machinery.; 9th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2017 ; Conference date: 31-07-2017 Through 03-08-2017",

year = "2017",

month = jul,

day = "31",

doi = "10.1145/3110025.3110090",

language = "English",

series = "Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2017",

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Gilani, Z, Farahbakhsh, R, Tyson, G, Wang, L & Crowcroft, J 2017, Of bots and humans (on twitter). in J Diesner, E Ferrari & G Xu (eds), Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2017. Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2017, Association for Computing Machinery, Inc, pp. 349-354, 9th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2017, Sydney, Australia, 31/07/17. https://doi.org/10.1145/3110025.3110090

Of bots and humans (on twitter). / Gilani, Zafar; Farahbakhsh, Reza; Tyson, Gareth et al.
Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2017. ed. / Jana Diesner; Elena Ferrari; Guandong Xu. Association for Computing Machinery, Inc, 2017. p. 349-354 (Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2017).

Research output: Chapter in Book/Conference Proceeding/Report › Conference Paper published in a book › peer-review

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AU - Tyson, Gareth

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AU - Crowcroft, Jon

PY - 2017/7/31

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N2 - Recent research has shown a substantial active presence of bots in online social networks (OSNs). In this paper we utilise our previous work (Stweeler) to comparatively analyse the usage and impact of bots and humans on Twitter, one of the largest OSNs in the world. We collect a large-scale Twitter dataset and define various metrics based on tweet metadata. Using a human annotation task we assign ‘bot’ and ‘human’ ground-truth labels to the dataset, and compare the annotations against an online bot detection tool for evaluation. We then ask a series of questions to discern important behavioural characteristics of bots and humans using metrics within and among four popularity groups. From the comparative analysis we draw differences and interesting similarities between the two entities, thus paving the way for reliable classification of bots, and studying automated political infiltration and advertisement campaigns.

AB - Recent research has shown a substantial active presence of bots in online social networks (OSNs). In this paper we utilise our previous work (Stweeler) to comparatively analyse the usage and impact of bots and humans on Twitter, one of the largest OSNs in the world. We collect a large-scale Twitter dataset and define various metrics based on tweet metadata. Using a human annotation task we assign ‘bot’ and ‘human’ ground-truth labels to the dataset, and compare the annotations against an online bot detection tool for evaluation. We then ask a series of questions to discern important behavioural characteristics of bots and humans using metrics within and among four popularity groups. From the comparative analysis we draw differences and interesting similarities between the two entities, thus paving the way for reliable classification of bots, and studying automated political infiltration and advertisement campaigns.

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Gilani Z, Farahbakhsh R, Tyson G, Wang L, Crowcroft J. Of bots and humans (on twitter). In Diesner J, Ferrari E, Xu G, editors, Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2017. Association for Computing Machinery, Inc. 2017. p. 349-354. (Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2017). doi: 10.1145/3110025.3110090

Of bots and humans (on twitter)

Abstract

Publication series

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Bibliographical note

Keywords

Access to Document

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