Multi-dimensional conversation analysis across online social networks

William Lucia, Cuneyt Gurcan Akcora, Elena Ferrari

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

With the advance of the Internet, ordinary users have created multiple personal accounts on online social networks, and interactions among these social network users have recently been tagged with location information. In this work, we observe user interactions across two popular online social networks, Facebook and Twitter, and analyze which factors lead to retweet/like interactions for tweets/posts. In addition to the named entities, lexical errors and expressed sentiments in these data items, we also consider the impact of shared user locations on user interactions. In particular, we show that geolocations of users can greatly affect which social network post/tweet will be liked/retweeted. We believe that the results of our analysis can help researchers to understand which social network content will have better visibility.

Original languageEnglish
Title of host publicationProceedings - 2013 IEEE 3rd International Conference on Cloud and Green Computing, CGC 2013 and 2013 IEEE 3rd International Conference on Social Computing and Its Applications, SCA 2013
Pages369-376
Number of pages8
DOIs
Publication statusPublished - 1 Dec 2013
Event3rd IEEE International Conference on Cloud and Green Computing, CGC 2013, Held Jointly with the 3rd IEEE International Conference on Social Computing and Its Applications, SCA 2013 - Karlsruhe
Duration: 30 Sep 20132 Oct 2013

Other

Other3rd IEEE International Conference on Cloud and Green Computing, CGC 2013, Held Jointly with the 3rd IEEE International Conference on Social Computing and Its Applications, SCA 2013
CityKarlsruhe
Period30/9/132/10/13

Fingerprint

Visibility
Internet

ASJC Scopus subject areas

  • Software

Cite this

Lucia, W., Akcora, C. G., & Ferrari, E. (2013). Multi-dimensional conversation analysis across online social networks. In Proceedings - 2013 IEEE 3rd International Conference on Cloud and Green Computing, CGC 2013 and 2013 IEEE 3rd International Conference on Social Computing and Its Applications, SCA 2013 (pp. 369-376). [6686057] https://doi.org/10.1109/CGC.2013.65

Multi-dimensional conversation analysis across online social networks. / Lucia, William; Akcora, Cuneyt Gurcan; Ferrari, Elena.

Proceedings - 2013 IEEE 3rd International Conference on Cloud and Green Computing, CGC 2013 and 2013 IEEE 3rd International Conference on Social Computing and Its Applications, SCA 2013. 2013. p. 369-376 6686057.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Lucia, W, Akcora, CG & Ferrari, E 2013, Multi-dimensional conversation analysis across online social networks. in Proceedings - 2013 IEEE 3rd International Conference on Cloud and Green Computing, CGC 2013 and 2013 IEEE 3rd International Conference on Social Computing and Its Applications, SCA 2013., 6686057, pp. 369-376, 3rd IEEE International Conference on Cloud and Green Computing, CGC 2013, Held Jointly with the 3rd IEEE International Conference on Social Computing and Its Applications, SCA 2013, Karlsruhe, 30/9/13. https://doi.org/10.1109/CGC.2013.65
Lucia W, Akcora CG, Ferrari E. Multi-dimensional conversation analysis across online social networks. In Proceedings - 2013 IEEE 3rd International Conference on Cloud and Green Computing, CGC 2013 and 2013 IEEE 3rd International Conference on Social Computing and Its Applications, SCA 2013. 2013. p. 369-376. 6686057 https://doi.org/10.1109/CGC.2013.65
Lucia, William ; Akcora, Cuneyt Gurcan ; Ferrari, Elena. / Multi-dimensional conversation analysis across online social networks. Proceedings - 2013 IEEE 3rd International Conference on Cloud and Green Computing, CGC 2013 and 2013 IEEE 3rd International Conference on Social Computing and Its Applications, SCA 2013. 2013. pp. 369-376
@inproceedings{a4bf6c2978034d21bcb62c5bc1edecc0,
title = "Multi-dimensional conversation analysis across online social networks",
abstract = "With the advance of the Internet, ordinary users have created multiple personal accounts on online social networks, and interactions among these social network users have recently been tagged with location information. In this work, we observe user interactions across two popular online social networks, Facebook and Twitter, and analyze which factors lead to retweet/like interactions for tweets/posts. In addition to the named entities, lexical errors and expressed sentiments in these data items, we also consider the impact of shared user locations on user interactions. In particular, we show that geolocations of users can greatly affect which social network post/tweet will be liked/retweeted. We believe that the results of our analysis can help researchers to understand which social network content will have better visibility.",
author = "William Lucia and Akcora, {Cuneyt Gurcan} and Elena Ferrari",
year = "2013",
month = "12",
day = "1",
doi = "10.1109/CGC.2013.65",
language = "English",
isbn = "9780769551142",
pages = "369--376",
booktitle = "Proceedings - 2013 IEEE 3rd International Conference on Cloud and Green Computing, CGC 2013 and 2013 IEEE 3rd International Conference on Social Computing and Its Applications, SCA 2013",

}

TY - GEN

T1 - Multi-dimensional conversation analysis across online social networks

AU - Lucia, William

AU - Akcora, Cuneyt Gurcan

AU - Ferrari, Elena

PY - 2013/12/1

Y1 - 2013/12/1

N2 - With the advance of the Internet, ordinary users have created multiple personal accounts on online social networks, and interactions among these social network users have recently been tagged with location information. In this work, we observe user interactions across two popular online social networks, Facebook and Twitter, and analyze which factors lead to retweet/like interactions for tweets/posts. In addition to the named entities, lexical errors and expressed sentiments in these data items, we also consider the impact of shared user locations on user interactions. In particular, we show that geolocations of users can greatly affect which social network post/tweet will be liked/retweeted. We believe that the results of our analysis can help researchers to understand which social network content will have better visibility.

AB - With the advance of the Internet, ordinary users have created multiple personal accounts on online social networks, and interactions among these social network users have recently been tagged with location information. In this work, we observe user interactions across two popular online social networks, Facebook and Twitter, and analyze which factors lead to retweet/like interactions for tweets/posts. In addition to the named entities, lexical errors and expressed sentiments in these data items, we also consider the impact of shared user locations on user interactions. In particular, we show that geolocations of users can greatly affect which social network post/tweet will be liked/retweeted. We believe that the results of our analysis can help researchers to understand which social network content will have better visibility.

UR - http://www.scopus.com/inward/record.url?scp=84893240975&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84893240975&partnerID=8YFLogxK

U2 - 10.1109/CGC.2013.65

DO - 10.1109/CGC.2013.65

M3 - Conference contribution

SN - 9780769551142

SP - 369

EP - 376

BT - Proceedings - 2013 IEEE 3rd International Conference on Cloud and Green Computing, CGC 2013 and 2013 IEEE 3rd International Conference on Social Computing and Its Applications, SCA 2013

ER -