TY - GEN
T1 - Is There Personalization in Twitter Search? A Study on polarized opinions about the Brazilian Welfare Reform
AU - Dos Santos, Jonatas C.
AU - Siqueira, Sean W.M.
AU - Nunes, Bernardo Pereira
AU - Balestrassi, Pedro P.
AU - Pereira, Fabricio R.S.
N1 - Publisher Copyright:
© 2020 ACM.
PY - 2020/7/6
Y1 - 2020/7/6
N2 - Personalization algorithms play an essential role in the way search platforms fetch results to users. While there are many empirical studies about the effects of these algorithms on Web searches like Google and Bing, reports about personalization on social media searches are rare. This exploratory study aims to understand and quantify the limits of personalization in Twitter search results. We developed a measurement methodology and agents to train a pair of polarized Twitter accounts and simultaneously collected search results from these accounts. The agents were run in a political context, the Brazilian Welfare Reform. Our findings show a significant amount of personalization differences when we compare search results from a new fresh profile to non-fresh ones. Peculiarly, little evidence for differences between two profiles that followed different accounts with polarized viewpoints about the same topic was found - the filter bubble hypothesis cannot be null.
AB - Personalization algorithms play an essential role in the way search platforms fetch results to users. While there are many empirical studies about the effects of these algorithms on Web searches like Google and Bing, reports about personalization on social media searches are rare. This exploratory study aims to understand and quantify the limits of personalization in Twitter search results. We developed a measurement methodology and agents to train a pair of polarized Twitter accounts and simultaneously collected search results from these accounts. The agents were run in a political context, the Brazilian Welfare Reform. Our findings show a significant amount of personalization differences when we compare search results from a new fresh profile to non-fresh ones. Peculiarly, little evidence for differences between two profiles that followed different accounts with polarized viewpoints about the same topic was found - the filter bubble hypothesis cannot be null.
KW - Personalization
KW - Social Media Search
KW - Twitter Search
UR - http://www.scopus.com/inward/record.url?scp=85088385192&partnerID=8YFLogxK
U2 - 10.1145/3394231.3397917
DO - 10.1145/3394231.3397917
M3 - Conference contribution
T3 - WebSci 2020 - Proceedings of the 12th ACM Conference on Web Science
SP - 267
EP - 276
BT - WebSci 2020 - Proceedings of the 12th ACM Conference on Web Science
PB - Association for Computing Machinery, Inc
T2 - 12th ACM Conference on Web Science, WebSci 2020
Y2 - 6 July 2020 through 10 July 2020
ER -