How analysis of mobile app reviews problematises linguistic approaches to internet troll detection

Abstract State-sponsored internet trolls repeat themselves in a unique way. They have a small number of messages to convey but they have to do it multiple times. Understandably, they are afraid of being repetitive because that will inevitably lead to their identification as trolls. Hence, their only...

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Autor principal:	Sergei Monakhov
Formato:	article
Lenguaje:	EN
Publicado:	Springer Nature 2021
Materias:	History of scholarship and learning. The humanities AZ20-999 Social Sciences H
Acceso en línea:	https://doaj.org/article/e2bd6c12a74e4ea9be3baad866032318
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spelling	oai:doaj.org-article:e2bd6c12a74e4ea9be3baad8660323182021-11-21T12:28:19ZHow analysis of mobile app reviews problematises linguistic approaches to internet troll detection10.1057/s41599-021-00968-72662-9992https://doaj.org/article/e2bd6c12a74e4ea9be3baad8660323182021-11-01T00:00:00Zhttps://doi.org/10.1057/s41599-021-00968-7https://doaj.org/toc/2662-9992Abstract State-sponsored internet trolls repeat themselves in a unique way. They have a small number of messages to convey but they have to do it multiple times. Understandably, they are afraid of being repetitive because that will inevitably lead to their identification as trolls. Hence, their only possible strategy is to keep diluting their target message with ever-changing filler words. That is exactly what makes them so susceptible to automatic detection. One serious challenge to this promising approach is posed by the fact that the same troll-like effect may arise as a result of collaborative repatterning that is not indicative of any malevolent practices in online communication. The current study addresses this issue by analysing more than 180,000 app reviews written in English and Russian and verifying the obtained results in the experimental setting where participants were asked to describe the same picture in two experimental conditions. The main finding of the study is that both observational and experimental samples became less troll-like as the time distance between their elements increased. Their ‘troll coefficient’ calculated as the ratio of the proportion of repeated content words among all content words to the proportion of repeated content word pairs among all content word pairs was found to be a function of time distance between separate individual contributions. These findings definitely render the task of developing efficient linguistic algorithms for internet troll detection more complicated. However, the problem can be alleviated by our ability to predict what the value of the troll coefficient of a certain group of texts would be if it depended solely on these texts’ creation time.Sergei MonakhovSpringer NaturearticleHistory of scholarship and learning. The humanitiesAZ20-999Social SciencesHENHumanities & Social Sciences Communications, Vol 8, Iss 1, Pp 1-10 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	History of scholarship and learning. The humanities AZ20-999 Social Sciences H
spellingShingle	History of scholarship and learning. The humanities AZ20-999 Social Sciences H Sergei Monakhov How analysis of mobile app reviews problematises linguistic approaches to internet troll detection
description	Abstract State-sponsored internet trolls repeat themselves in a unique way. They have a small number of messages to convey but they have to do it multiple times. Understandably, they are afraid of being repetitive because that will inevitably lead to their identification as trolls. Hence, their only possible strategy is to keep diluting their target message with ever-changing filler words. That is exactly what makes them so susceptible to automatic detection. One serious challenge to this promising approach is posed by the fact that the same troll-like effect may arise as a result of collaborative repatterning that is not indicative of any malevolent practices in online communication. The current study addresses this issue by analysing more than 180,000 app reviews written in English and Russian and verifying the obtained results in the experimental setting where participants were asked to describe the same picture in two experimental conditions. The main finding of the study is that both observational and experimental samples became less troll-like as the time distance between their elements increased. Their ‘troll coefficient’ calculated as the ratio of the proportion of repeated content words among all content words to the proportion of repeated content word pairs among all content word pairs was found to be a function of time distance between separate individual contributions. These findings definitely render the task of developing efficient linguistic algorithms for internet troll detection more complicated. However, the problem can be alleviated by our ability to predict what the value of the troll coefficient of a certain group of texts would be if it depended solely on these texts’ creation time.
format	article
author	Sergei Monakhov
author_facet	Sergei Monakhov
author_sort	Sergei Monakhov
title	How analysis of mobile app reviews problematises linguistic approaches to internet troll detection
title_short	How analysis of mobile app reviews problematises linguistic approaches to internet troll detection
title_full	How analysis of mobile app reviews problematises linguistic approaches to internet troll detection
title_fullStr	How analysis of mobile app reviews problematises linguistic approaches to internet troll detection
title_full_unstemmed	How analysis of mobile app reviews problematises linguistic approaches to internet troll detection
title_sort	how analysis of mobile app reviews problematises linguistic approaches to internet troll detection
publisher	Springer Nature
publishDate	2021
url	https://doaj.org/article/e2bd6c12a74e4ea9be3baad866032318
work_keys_str_mv	AT sergeimonakhov howanalysisofmobileappreviewsproblematiseslinguisticapproachestointernettrolldetection
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How analysis of mobile app reviews problematises linguistic approaches to internet troll detection

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