Abstract

The advent of participatory web has enabled information consumers to become information producers via social media. This phenomenon has attracted researchers of different disciplines including social scientists, political parties, and market researchers to study social media as a source of data to explain human behavior in the physical world. Could the traditional approaches of studying social behaviors such as surveys be complemented by computational studies that use massive user-generated data in social media? In this paper, using a large amount of data collected from Twitter, the blogosphere, social networks, and news sources, we perform preliminary research to investigate if human behavior in the real world can be understood by analyzing social media data. The goals of this research is twofold: (1) determining the relative effectiveness of a social media lens in analyzing and predicting real-world collective behavior, and (2) exploring the domains and situations under which social media can be a predictor for real-world's behavior. We develop a four-step model: community selection, data collection, online behavior analysis, and behavior prediction. The results of this study show that in most cases social media is a good tool for estimating attitudes and further research is needed for predicting social behavior.

Original languageEnglish (US)
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Pages18-26
Number of pages9
Volume7227 LNCS
DOIs
StatePublished - 2012
Event5th International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction, SBP 2012 - College Park, MD, United States
Duration: Apr 3 2012Apr 5 2012

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume7227 LNCS
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other5th International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction, SBP 2012
CountryUnited States
CityCollege Park, MD
Period4/3/124/5/12

Fingerprint

Social Media
Lens
Lenses
Social Behavior
Human Behavior
Collective Behavior
Social Networks
Predictors
Prediction

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

Cite this

Abbasi, M. A., Chai, S. K., Liu, H., & Sagoo, K. (2012). Real-world behavior analysis through a social media lens. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7227 LNCS, pp. 18-26). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7227 LNCS). https://doi.org/10.1007/978-3-642-29047-3_3

Real-world behavior analysis through a social media lens. / Abbasi, Mohammad Ali; Chai, Sun Ki; Liu, Huan; Sagoo, Kiran.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 7227 LNCS 2012. p. 18-26 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7227 LNCS).

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

Abbasi, MA, Chai, SK, Liu, H & Sagoo, K 2012, Real-world behavior analysis through a social media lens. in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). vol. 7227 LNCS, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 7227 LNCS, pp. 18-26, 5th International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction, SBP 2012, College Park, MD, United States, 4/3/12. https://doi.org/10.1007/978-3-642-29047-3_3
Abbasi MA, Chai SK, Liu H, Sagoo K. Real-world behavior analysis through a social media lens. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 7227 LNCS. 2012. p. 18-26. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-642-29047-3_3
Abbasi, Mohammad Ali ; Chai, Sun Ki ; Liu, Huan ; Sagoo, Kiran. / Real-world behavior analysis through a social media lens. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 7227 LNCS 2012. pp. 18-26 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
@inproceedings{ced0c5b4d99941d58dd3f77025aee8a5,
title = "Real-world behavior analysis through a social media lens",
abstract = "The advent of participatory web has enabled information consumers to become information producers via social media. This phenomenon has attracted researchers of different disciplines including social scientists, political parties, and market researchers to study social media as a source of data to explain human behavior in the physical world. Could the traditional approaches of studying social behaviors such as surveys be complemented by computational studies that use massive user-generated data in social media? In this paper, using a large amount of data collected from Twitter, the blogosphere, social networks, and news sources, we perform preliminary research to investigate if human behavior in the real world can be understood by analyzing social media data. The goals of this research is twofold: (1) determining the relative effectiveness of a social media lens in analyzing and predicting real-world collective behavior, and (2) exploring the domains and situations under which social media can be a predictor for real-world's behavior. We develop a four-step model: community selection, data collection, online behavior analysis, and behavior prediction. The results of this study show that in most cases social media is a good tool for estimating attitudes and further research is needed for predicting social behavior.",
author = "Abbasi, {Mohammad Ali} and Chai, {Sun Ki} and Huan Liu and Kiran Sagoo",
year = "2012",
doi = "10.1007/978-3-642-29047-3_3",
language = "English (US)",
isbn = "9783642290466",
volume = "7227 LNCS",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
pages = "18--26",
booktitle = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

}

TY - GEN

T1 - Real-world behavior analysis through a social media lens

AU - Abbasi, Mohammad Ali

AU - Chai, Sun Ki

AU - Liu, Huan

AU - Sagoo, Kiran

PY - 2012

Y1 - 2012

N2 - The advent of participatory web has enabled information consumers to become information producers via social media. This phenomenon has attracted researchers of different disciplines including social scientists, political parties, and market researchers to study social media as a source of data to explain human behavior in the physical world. Could the traditional approaches of studying social behaviors such as surveys be complemented by computational studies that use massive user-generated data in social media? In this paper, using a large amount of data collected from Twitter, the blogosphere, social networks, and news sources, we perform preliminary research to investigate if human behavior in the real world can be understood by analyzing social media data. The goals of this research is twofold: (1) determining the relative effectiveness of a social media lens in analyzing and predicting real-world collective behavior, and (2) exploring the domains and situations under which social media can be a predictor for real-world's behavior. We develop a four-step model: community selection, data collection, online behavior analysis, and behavior prediction. The results of this study show that in most cases social media is a good tool for estimating attitudes and further research is needed for predicting social behavior.

AB - The advent of participatory web has enabled information consumers to become information producers via social media. This phenomenon has attracted researchers of different disciplines including social scientists, political parties, and market researchers to study social media as a source of data to explain human behavior in the physical world. Could the traditional approaches of studying social behaviors such as surveys be complemented by computational studies that use massive user-generated data in social media? In this paper, using a large amount of data collected from Twitter, the blogosphere, social networks, and news sources, we perform preliminary research to investigate if human behavior in the real world can be understood by analyzing social media data. The goals of this research is twofold: (1) determining the relative effectiveness of a social media lens in analyzing and predicting real-world collective behavior, and (2) exploring the domains and situations under which social media can be a predictor for real-world's behavior. We develop a four-step model: community selection, data collection, online behavior analysis, and behavior prediction. The results of this study show that in most cases social media is a good tool for estimating attitudes and further research is needed for predicting social behavior.

UR - http://www.scopus.com/inward/record.url?scp=84859112163&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84859112163&partnerID=8YFLogxK

U2 - 10.1007/978-3-642-29047-3_3

DO - 10.1007/978-3-642-29047-3_3

M3 - Conference contribution

SN - 9783642290466

VL - 7227 LNCS

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 18

EP - 26

BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

ER -