TY - GEN
T1 - Wearing many (social) hats
T2 - 11th International Conference on Web and Social Media, ICWSM 2017
AU - Zhong, Changtao
AU - Chang, Hau Wen
AU - Karamshuk, Dmytro
AU - Lee, Dongwon
AU - Sastry, Nishanth
N1 - Funding Information:
∗This work was partially supported by the Space for Sharing (S4S) project (Grant No. ES/M00354X/1). This research was also in part supported by NSF CNS-1422215, NSF IUSE-1525601, and Samsung GRO 2015 awards. †Work done while at King’s College London ‡Work done while at Penn State University §Work done while at King’s College London Copyright ©c 2017, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.
PY - 2017
Y1 - 2017
N2 - This paper investigates when users create profiles in different social networks, whether they are redundant expressions of the same persona, or they are adapted to each platform. Using the personal webpages of 116,998 users on About.me, we identify and extract matched user profiles on several major social networks including Facebook, Twitter, LinkedIn, and Instagram. We find evidence for distinct site-specific norms, such as differences in the language used in the text of the profile self-description, and the kind of picture used as profile image. By learning a model that robustly identifies the platform given a user's profile image (0.657-0.829 AUC) or self-description (0.608-0.847 AUC), we confirm that users do adapt their behaviour to individual platforms in an identifiable and learnable manner. However, different genders and age groups adapt their behaviour differently from each other, and these differences are, in general, consistent across different platforms. We show that differences in social profile construction correspond to differences in how formal or informal the platform is.
AB - This paper investigates when users create profiles in different social networks, whether they are redundant expressions of the same persona, or they are adapted to each platform. Using the personal webpages of 116,998 users on About.me, we identify and extract matched user profiles on several major social networks including Facebook, Twitter, LinkedIn, and Instagram. We find evidence for distinct site-specific norms, such as differences in the language used in the text of the profile self-description, and the kind of picture used as profile image. By learning a model that robustly identifies the platform given a user's profile image (0.657-0.829 AUC) or self-description (0.608-0.847 AUC), we confirm that users do adapt their behaviour to individual platforms in an identifiable and learnable manner. However, different genders and age groups adapt their behaviour differently from each other, and these differences are, in general, consistent across different platforms. We show that differences in social profile construction correspond to differences in how formal or informal the platform is.
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M3 - Conference contribution
AN - SCOPUS:85029443500
T3 - Proceedings of the 11th International Conference on Web and Social Media, ICWSM 2017
SP - 397
EP - 406
BT - Proceedings of the 11th International Conference on Web and Social Media, ICWSM 2017
PB - AAAI press
Y2 - 15 May 2017 through 18 May 2017
ER -