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Incredible tweets: Automated credibility analysis in Twitter feeds using an alternating decision tree algorithm
KTH, School of Computer Science and Communication (CSC).
2016 (English)Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesis
Abstract [en]

This project investigates how to determine the credibility of a tweet without using human perception. Information about the user and the tweet is studied in search for correlations between their properties and the credibility of the tweet. An alternating decision tree is created to automatically determine the credibility of tweets.

Some features are found to correlate to the credibility of the tweets, amongst which the number of previous tweets by a user and the use of uppercase characters are the most prominent.

Place, publisher, year, edition, pages
2016.
National Category
Computer Science
Identifiers
URN: urn:nbn:se:kth:diva-186711OAI: oai:DiVA.org:kth-186711DiVA: diva2:927807
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Available from: 2016-05-18 Created: 2016-05-13 Last updated: 2016-05-18Bibliographically approved

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fulltext(836 kB)70 downloads
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File name FULLTEXT01.pdfFile size 836 kBChecksum SHA-512
584bb528aab7ff022fec88e62dfd0a6b72df9090eb9fa50f2449a20f21ddf6de90638c9f00567a065bac6d6aec33173f32e1b1279c759018af39981e9abca883
Type fulltextMimetype application/pdf

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CiteExportLink to record
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Citation style
  • apa
  • harvard1
  • ieee
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More languages
Output format
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