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A Semi-automatic Approach for Labeling Large Amounts of Automated and Non-automated Social Media User Accounts
KTH.
2015 (English)In: Proceedings - 2nd European Network Intelligence Conference, ENIC 2015, Institute of Electrical and Electronics Engineers (IEEE), 2015, 155-159 p.Conference paper (Refereed)Text
Abstract [en]

Automated accounts are used for many purposes in social media, including sending spam, spreading of viruses and conducting psychological operations in political or military conflicts. While several previous attempts have been made to classify bot accounts in the spam domain, there are (to the best of our knowledge) no previous studies on detection of automated accounts in a military information operation context. Traditional machine learning approaches to bot detection rely on manual annotation of training sets from which classifiers can be learnt, which requires a large manual effort. We present a semi automated alternative to manual annotation which significantly reduces the effort and resources needed, and hence speeds up the process of adapting classifiers to new domains. Our application of the method to Twitter data from the Russia-Ukraine conflict and our classification results suggest that good classification performance still can be obtained despite generating training sets semi-automatically rather than using manual annotation.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2015. 155-159 p.
Keyword [en]
bot detection, information operations, semi-automatic labeling, social media analysis, Artificial intelligence, Classification (of information), Internet, Learning systems, Social networking (online), Viruses, Bot detections, Classification performance, Classification results, Machine learning approaches, Military conflicts, Semi-automatics, Automation
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:kth:diva-186838DOI: 10.1109/ENIC.2015.31ScopusID: 2-s2.0-84964589015ISBN: 9781467375924OAI: oai:DiVA.org:kth-186838DiVA: diva2:937498
Conference
2nd European Network Intelligence Conference, ENIC 2015, 21 September 2015 through 22 September 2015
Note

QC 20160615

Available from: 2016-06-15 Created: 2016-05-13 Last updated: 2016-06-15Bibliographically approved

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Publisher's full textScopusA Semi-automatic Approach for Labeling Large Amounts of Automated and Non-automated Social Media User Accounts

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Teljstedt, Christopher
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KTH
Electrical Engineering, Electronic Engineering, Information Engineering

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ReferencesLink to record
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