Link Prediction on Tweets’ Content (CROSBI ID 639714)
Prilog sa skupa u časopisu | izvorni znanstveni rad | međunarodna recenzija
Podaci o odgovornosti
Martinčić-Ipšić, Sanda ; Močibob, Edvin ; Meštrović, Ana
engleski
Link Prediction on Tweets’ Content
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.
tweets ; link prediction complex networks ; language networks
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Podaci o prilogu
559-567.
2016.
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objavljeno
10.1007/978-3-319-46254-7_45
Podaci o matičnoj publikaciji
Communications in computer and information science
Dregvaite, G. ; Damasevicius, R.
Berlin: Springer
978-3-319-46254-7
1865-0929
Podaci o skupu
22nd International Conference, ICIST 2016
predavanje
11.10.2016-13.10.2016
Druskininkai, Litva
Povezanost rada
Informacijske i komunikacijske znanosti, Računarstvo