TY - GEN
T1 - Temporal Properties of Cyberbullying on Instagram
AU - Gupta, Aabhaas
AU - Yang, Wenxi
AU - Sivakumar, Divya
AU - Silva, Yasin
AU - Hall, Deborah
AU - Nardini Barioni, Maria
N1 - Funding Information:
This material is based upon work supported by the National Science Foundation (NSF) grant 1719722.
Publisher Copyright:
© 2020 ACM.
PY - 2020/4/20
Y1 - 2020/4/20
N2 - Concurrent with the growth and widespread use of social networking platforms has been a rise in the prevalence of cyberbullying and cyberharassment, particularly among youth. Although cyberbullying is frequently defined as hostile communication or interactions that occur repetitively via electronic media, little is known about the temporal aspects of cyberbullying on social media, such as how the number, frequency, and timing of posts may vary systematically between cyberbullying and non-cyberbullying social media sessions. In this paper, we aim to contribute to the understanding of temporal properties of cyberbullying through the analysis of Instagram data. That is, the paper presents key temporal characteristics of cyberbullying and trends obtained from descriptive and burst analysis tasks. Our results have the potential to inform the development of more effective cyberbullying detection models.
AB - Concurrent with the growth and widespread use of social networking platforms has been a rise in the prevalence of cyberbullying and cyberharassment, particularly among youth. Although cyberbullying is frequently defined as hostile communication or interactions that occur repetitively via electronic media, little is known about the temporal aspects of cyberbullying on social media, such as how the number, frequency, and timing of posts may vary systematically between cyberbullying and non-cyberbullying social media sessions. In this paper, we aim to contribute to the understanding of temporal properties of cyberbullying through the analysis of Instagram data. That is, the paper presents key temporal characteristics of cyberbullying and trends obtained from descriptive and burst analysis tasks. Our results have the potential to inform the development of more effective cyberbullying detection models.
KW - Instagram
KW - burst analysis
KW - cyberbullying
KW - social media
KW - temporal properties
UR - http://www.scopus.com/inward/record.url?scp=85091703959&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85091703959&partnerID=8YFLogxK
U2 - 10.1145/3366424.3385771
DO - 10.1145/3366424.3385771
M3 - Conference contribution
AN - SCOPUS:85091703959
T3 - The Web Conference 2020 - Companion of the World Wide Web Conference, WWW 2020
SP - 576
EP - 583
BT - The Web Conference 2020 - Companion of the World Wide Web Conference, WWW 2020
PB - Association for Computing Machinery
T2 - 29th International World Wide Web Conference, WWW 2020
Y2 - 20 April 2020 through 24 April 2020
ER -