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International Conference on Information and Software Technologies

ICIST 2016: Information and Software Technologies pp 559–567Cite as

Link Prediction on Tweets’ Content

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Part of the book series: Communications in Computer and Information Science ((CCIS,volume 639))

Abstract

In this paper we test various weighted local similarity network measures for predicting the future content of tweets. Our aim is to determine the most suitable measure for predicting new content in tweets and subsequently explore the spreading positively and negatively oriented content on Twitter. The tweets in the English language were collected via the Twitter API depending on their content. That is, we searched for the tweets containing specific predefined keywords from different domains - positive or negative. From the gathered tweets the weighted complex network of words is formed, where nodes represent words and a link between two nodes exists if these two words co-occur in the same tweet, while the weight denotes the co-occurrence frequency. For the link prediction task we study five local similarity network measures commonly used in unweighted networks (Common Neighbors, Jaccard Coefficient, Preferential Attachment, Adamic Adar and Resource Allocation Index) which we have adapted to weighted networks. Finally, we evaluated all the modified measures in terms of the precision of predicted links. The obtained results suggest that the Weighted Resource Allocation Index has the best potential for the prediction of content in tweets.

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Notes

  1. 1.

    Stopwords are a list of the most common, short function words that do not carry strong semantic properties, but are needed for the syntax of a language (pronouns, prepositions, conjunctions, abbreviations, ...).

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Author information

Authors and Affiliations

  1. Department of Informatics, University of Rijeka, Radmile Matejčić 2, 51000, Rijeka, Croatia

    Sanda Martinčić-Ipšić, Edvin Močibob & Ana Meštrović

Authors
  1. Sanda Martinčić-Ipšić
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  2. Edvin Močibob
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  3. Ana Meštrović
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Corresponding author

Correspondence to Sanda Martinčić-Ipšić .

Editor information

Editors and Affiliations

  1. Kaunas University of Technology , Kaunas, Lithuania

    Giedre Dregvaite

  2. Kaunas University of Technology , Kaunas, Lithuania

    Robertas Damasevicius

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© 2016 Springer International Publishing Switzerland

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Martinčić-Ipšić, S., Močibob, E., Meštrović, A. (2016). Link Prediction on Tweets’ Content. In: Dregvaite, G., Damasevicius, R. (eds) Information and Software Technologies. ICIST 2016. Communications in Computer and Information Science, vol 639. Springer, Cham. https://doi.org/10.1007/978-3-319-46254-7_45

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  • DOI: https://doi.org/10.1007/978-3-319-46254-7_45

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-46253-0

  • Online ISBN: 978-3-319-46254-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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