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Title:Link prediction on Twitter
Authors:ID Martinčić-Ipšić, Sanda (Author)
ID Močibob, Edvin (Author)
ID Perc, Matjaž (Author)
Files:.pdf PLOS_ONE_2017_Martincic-Ipsic,_Mocibob,_Perc_Link_prediction_on_Twitter.pdf (6,98 MB)
MD5: FFB5102AB1CE73A868DC8BE90066D6B5
PID: 20.500.12556/dkum/c0137cd1-77c2-45bb-82d0-59cfc14babb0
 
URL http://dx.plos.org/10.1371/journal.pone.0181079
 
Language:English
Work type:Article
Typology:1.01 - Original Scientific Article
Organization:FNM - Faculty of Natural Sciences and Mathematics
Abstract:With over 300 million active users, Twitter is among the largest online news and social networking services in existence today. Open access to information on Twitter makes it a valuable source of data for research on social interactions, sentiment analysis, content diffusion, link prediction, and the dynamics behind human collective behaviour in general. Here we use Twitter data to construct co-occurrence language networks based on hashtags and based on all the words in tweets, and we use these networks to study link prediction by means of different methods and evaluation metrics. In addition to using five known methods, we propose two effective weighted similarity measures, and we compare the obtained outcomes in dependence on the selected semantic context of topics on Twitter. We find that hashtag networks yield to a large degree equal results as all-word networks, thus supporting the claim that hashtags alone robustly capture the semantic context of tweets, and as such are useful and suitable for studying the content and categorization. We also introduce ranking diagrams as an efficient tool for the comparison of the performance of different link prediction algorithms across multiple datasets. Our research indicates that successful link prediction algorithms work well in correctly foretelling highly probable links even if the information about a network structure is incomplete, and they do so even if the semantic context is rationalized to hashtags.
Keywords:link prediction, data mining, Twitter, network analysis
Publication status:Published
Publication version:Version of Record
Year of publishing:2017
Number of pages:str. 1-21
Numbering:Letn. 12, št. 7
PID:20.500.12556/DKUM-68299 New window
ISSN:1932-6203
UDC:53
ISSN on article:1932-6203
COBISS.SI-ID:23280136 New window
DOI:10.1371/journal.pone.0181079 New window
NUK URN:URN:SI:UM:DK:TTXEWLPK
Publication date in DKUM:15.09.2017
Views:1864
Downloads:212
Metadata:XML DC-XML DC-RDF
Categories:Misc.
:
MARTINČIĆ-IPŠIĆ, Sanda, MOČIBOB, Edvin and PERC, Matjaž, 2017, Link prediction on Twitter. PloS one [online]. 2017. Vol. 12, no. 7, p. 1–21. [Accessed 15 March 2025]. DOI 10.1371/journal.pone.0181079. Retrieved from: https://dk.um.si/IzpisGradiva.php?lang=eng&id=68299
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Record is a part of a journal

Title:PloS one
Publisher:Public Library of Science
ISSN:1932-6203
COBISS.SI-ID:2005896 New window

Document is financed by a project

Funder:ARRS - Slovenian Research Agency
Project number:J1-7009
Name:Fazni prehodi proti kooperaciji v sklopljenih populacijah

Licences

License:CC BY 4.0, Creative Commons Attribution 4.0 International
Link:http://creativecommons.org/licenses/by/4.0/
Description:This is the standard Creative Commons license that gives others maximum freedom to do what they want with the work as long as they credit the author.
Licensing start date:15.09.2017

Secondary language

Language:Slovenian
Keywords:napovedovanje povezav, rudarjenje podatkov, Twitter, analiza omrežja


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