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Naslov:Link prediction on Twitter
Avtorji:Martinčić-Ipšić, Sanda (Avtor)
Močibob, Edvin (Avtor)
Perc, Matjaž (Avtor)
Datoteke:.pdf PLOS_ONE_2017_Martincic-Ipsic,_Mocibob,_Perc_Link_prediction_on_Twitter.pdf (6,98 MB)
MD5: FFB5102AB1CE73A868DC8BE90066D6B5
 
URL http://dx.plos.org/10.1371/journal.pone.0181079
 
Jezik:Angleški jezik
Vrsta gradiva:Znanstveno delo (r2)
Tipologija:1.01 - Izvirni znanstveni članek
Organizacija:FNM - Fakulteta za naravoslovje in matematiko
Opis: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.
Ključne besede:link prediction, data mining, Twitter, network analysis
Leto izida:2017
Št. strani:str. 1-21
Številčenje:št. 7, Letn. 12
ISSN:1932-6203
UDK:53
COBISS_ID:23280136 Novo okno
DOI:10.1371/journal.pone.0181079 Novo okno
ISSN pri članku:1932-6203
NUK URN:URN:SI:UM:DK:TTXEWLPK
Število ogledov:847
Število prenosov:87
Metapodatki:XML RDF-CHPDL DC-XML DC-RDF
Področja:Ostalo
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Skupna ocena:(0 glasov)
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Gradivo je del revije

Naslov:PloS one
Založnik:Public Library of Science
ISSN:1932-6203
COBISS.SI-ID:2005896 Novo okno

Gradivo je financirano iz projekta

Financer:ARRS - Agencija za raziskovalno dejavnost Republike Slovenije (ARRS)
Številka projekta:J1-7009
Naslov:Fazni prehodi proti kooperaciji v sklopljenih populacijah

Licence

Licenca:CC BY 4.0, Creative Commons Priznanje avtorstva 4.0 Mednarodna
Povezava:http://creativecommons.org/licenses/by/4.0/deed.sl
Opis:To je standardna licenca Creative Commons, ki daje uporabnikom največ možnosti za nadaljnjo uporabo dela, pri čemer morajo navesti avtorja.
Začetek licenciranja:15.09.2017

Sekundarni jezik

Jezik:Slovenski jezik
Ključne besede:napovedovanje povezav, rudarjenje podatkov, Twitter, analiza omrežja


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