TY - GEN
T1 - The million tweets fallacy
T2 - 12th International AAAI Conference on Web and Social Media, ICWSM 2018
AU - Vilenchik, Dan
N1 - Publisher Copyright: Copyright © 2018, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.
PY - 2018/1/1
Y1 - 2018/1/1
N2 - In this paper we point to a surprising phenomenon in Online Social Networks (OSNs), which we call “The Million Tweets Fallacy”. Our hypothesis is that the measurements of activity of a user in an OSN are not correlated with the measurements of feedback that the user receives on that activity. For example, the number of tweets is uncorrelated with the number of retweets or likes. In other words, a voluminously tweeting user is not necessarily fuelled by the attention he gets from his followers. An innovative aspect of this work is that we treat “activity” and “feedback” as multidimensional axes, and do not reduce the problem to a one-dimensional pairwise correlation problem. We apply our methodology to six OSNs. For Twitter, Instagram, LinkedIn and Steam we gathered the data ourselves, collecting features that cover both users' activity and feedback in the OSN. For YouTube and Flickr we used existing data from the literature. In all OSNs, with the only exception of Steam, we confirmed our hypothesis.
AB - In this paper we point to a surprising phenomenon in Online Social Networks (OSNs), which we call “The Million Tweets Fallacy”. Our hypothesis is that the measurements of activity of a user in an OSN are not correlated with the measurements of feedback that the user receives on that activity. For example, the number of tweets is uncorrelated with the number of retweets or likes. In other words, a voluminously tweeting user is not necessarily fuelled by the attention he gets from his followers. An innovative aspect of this work is that we treat “activity” and “feedback” as multidimensional axes, and do not reduce the problem to a one-dimensional pairwise correlation problem. We apply our methodology to six OSNs. For Twitter, Instagram, LinkedIn and Steam we gathered the data ourselves, collecting features that cover both users' activity and feedback in the OSN. For YouTube and Flickr we used existing data from the literature. In all OSNs, with the only exception of Steam, we confirmed our hypothesis.
UR - https://www.scopus.com/pages/publications/85050587021
M3 - Conference contribution
T3 - 12th International AAAI Conference on Web and Social Media, ICWSM 2018
SP - 688
EP - 691
BT - 12th International AAAI Conference on Web and Social Media, ICWSM 2018
Y2 - 25 June 2018 through 28 June 2018
ER -