Pregled bibliografske jedinice broj: 819179
Revealing the structure of domain specific tweets via complex networks analysis
Revealing the structure of domain specific tweets via complex networks analysis // 39th International Convention on Information and Communication Technology, Electronics and Microelectronics, MIPRO 2016 / Biljanović, Petar (ur.).
Opatija: Institute of Electrical and Electronics Engineers (IEEE), 2016. str. 1904-1908 doi:10.1109/MIPRO.2016.7522398 (predavanje, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)
CROSBI ID: 819179 Za ispravke kontaktirajte CROSBI podršku putem web obrasca
Naslov
Revealing the structure of domain specific tweets
via complex networks analysis
Autori
Močibob, Edvin ; Martinčić-Ipšić, Sanda ; Meštrović, Ana
Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni
Izvornik
39th International Convention on Information and Communication Technology, Electronics and Microelectronics, MIPRO 2016
/ Biljanović, Petar - Opatija : Institute of Electrical and Electronics Engineers (IEEE), 2016, 1904-1908
ISBN
978-953-233-087-8
Skup
39th International Convention on Information and Communication Technology, Electronics and Microelectronics, MIPRO 2016
Mjesto i datum
Opatija, Hrvatska, 2016
Vrsta sudjelovanja
Predavanje
Vrsta recenzije
Međunarodna recenzija
Ključne riječi
weighted complex networks ; tweets ; link prediction ; tweet polarity
(eighted complex networks ; tweets ; link prediction ; tweet polarity)
Sažetak
In this paper we explore the relation between different groups of tweets using complex network analysis and link prediction. The tweets were collected via the Twitter API depending on their textual content. That is, we searched for the tweets in English language containing specific predefined keywords from different domains. From the gathered tweets a complex network of words was formed as a weighted network. Nodes represent words and a link between two nodes exists if these two words co-occur in the same tweet, while weight denotes the co-occurrence frequency. The Twitter search was repeated for four different search criteria (API queries based on different tweet keywords), thus resulting in four networks with different nodes and links. The resulting networks were subjects to further network analysis, as comparison of numerical properties for different networks and link prediction for individual networks. This paper shows the tweet scraping process, our approach to building the networks, the measures we calculated for them, the differences and similarities between different networks we built and our success in predicting future links.
Izvorni jezik
Engleski
Znanstvena područja
Računarstvo, Informacijske i komunikacijske znanosti
POVEZANOST RADA
Ustanove:
Fakultet informatike i digitalnih tehnologija, Rijeka
Citiraj ovu publikaciju:
Časopis indeksira:
- Web of Science Core Collection (WoSCC)
- Conference Proceedings Citation Index - Science (CPCI-S)
- Scopus