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Early detection of internet trolls: Introducing an algorithm based on word pairs / single words multiple repetition ratio
- Source :
- PLoS ONE, PLoS ONE, Vol 15, Iss 8, p e0236832 (2020)
- Publication Year :
- 2020
- Publisher :
- Public Library of Science, 2020.
-
Abstract
- Troll internet messages, especially those posted on Twitter, have recently been recognised as a very powerful weapon in hybrid warfare. Hence, an important task for the academic community is to provide a tool for identifying internet troll accounts as quickly as possible. At the same time, this tool must be highly accurate so that its employment will not violate people's rights and affect the freedom of speech. Though such a task can be effectively fulfilled on purely linguistic grounds, as of yet, very little work has been done that could help to explain the discourse-specific features of this type of writing. In this paper, we suggest a quantitative measure for identifying troll messages which is based on taking into account certain sociolinguistic limitations of troll speech, and discuss two algorithms that both require as few as 50 tweets to establish the true nature of the tweets, whether 'genuine' or 'troll-like'.
- Subjects :
- Computer science
Social Sciences
02 engineering and technology
Task (project management)
Mathematical and Statistical Techniques
Sociology
0202 electrical engineering, electronic engineering, information engineering
Psychology
Computer Networks
Language
Multidisciplinary
Repetition (rhetorical device)
05 social sciences
Statistics
Social Communication
Semantics
Social Networks
Physical Sciences
Medicine
Regression Analysis
The Internet
Affect (linguistics)
Algorithm
Network Analysis
Algorithms
Research Article
050101 languages & linguistics
Computer and Information Sciences
Science
Twitter
Linear Regression Analysis
Research and Analysis Methods
020204 information systems
Speech
Humans
0501 psychology and cognitive sciences
Statistical Methods
Internet
business.industry
Cognitive Psychology
Biology and Life Sciences
Linguistics
Communications
Sociolinguistics
Cognitive Science
business
Social Media
Mathematics
Neuroscience
Subjects
Details
- Language :
- English
- ISSN :
- 19326203
- Volume :
- 15
- Issue :
- 8
- Database :
- OpenAIRE
- Journal :
- PLoS ONE
- Accession number :
- edsair.doi.dedup.....8c6a385a5e7e3720841354acd3abb27e