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COMBATING DISINFORMATION: Detecting fake news with linguistic models and classification algorithms
KTH, School of Computer Science and Communication (CSC).
KTH, School of Computer Science and Communication (CSC).
2017 (English)Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesisAlternative title
BEKÄMPNING AV DISINFORMATION : Upptäcka falska nyheter med språkliga modeller och klassificeringsalgoritmer (Swedish)
Abstract [en]

The purpose of this study is to examine the possibility of accurately distinguishing fabricated news from authentic news stories using Naive Bayes classification algorithms. This involves a comparative study of two different machine learning classification algorithms. The work also contains an overview of how linguistic text analytics can be utilized in detection purposes and an attempt to extract interesting information was made using Word Frequencies. A discussion of how different actors and parties in businesses and governments are affected by and how they handle deception caused by fake news articles was also made. This study further tries to ascertain what collective steps could be made towards introducing a functioning solution to combat fake news. The result swere inconclusive and the simple Naive Bayes algorithms used did not yieldfully satisfactory results. Word frequencies alone did not give enough information for detection. They were however found to be potentially useful as part of a larger set of algorithms and strategies as part of a solution to handling of misinformation.

Abstract [sv]

Syftet med denna studie är att undersöka möjligheten att på ett pålitligt sättskilja mellan fabricerade och autentiska nyheter med hjälp av Naive bayesalgoritmer,detta involverar en komparativ studie mellan två olika typer avalgoritmer. Arbetet innehåller även en översikt över hur lingvistisk textanalyskan användas för detektion och ett försök gjordes att extrahera information medhjälp av ordfrekvenser. Det förs även en diskussion kring hur de olika aktörernaoch parterna inom näringsliv och regeringar påverkas av och hur de hanterarbedrägeri kopplat till falska nyheter. Studien försöker vidare undersöka vilkasteg som kan tas mot en fungerande lösning för att motarbeta falska nyheter. Algoritmernagav i slutändan otillfredställande resultat och ordfrekvenserna kundeinte ensamma ge nog med information. De tycktes dock potentiellt användbarasom en del i ett större maskineri av algoritmer och strategier ämnade att hanteradesinformation.

Place, publisher, year, edition, pages
2017.
Keyword [en]
fake news, machine learning, naive bayes
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:kth:diva-209755OAI: oai:DiVA.org:kth-209755DiVA, id: diva2:1114109
Educational program
Master of Science in Engineering - Industrial Engineering and Management
Supervisors
Examiners
Available from: 2017-10-16 Created: 2017-06-22 Last updated: 2018-01-13Bibliographically approved

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