TY - JOUR

T1 - Unveiling the silent majority

T2 - stance detection and characterization of passive users on social media using collaborative filtering and graph convolutional networks

AU - Zhou, Zhiwei

AU - Elejalde, Erick

N1 - Funding Information: Open Access funding enabled and organized by Projekt DEAL. This paper is part of a project that has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 101021866 (CRiTERIA).

PY - 2024/4/4

Y1 - 2024/4/4

N2 - Social Media (SM) has become a popular medium for individuals to share their opinions on various topics, including politics, social issues, and daily affairs. During controversial events such as political elections, active users often proclaim their stance and try to persuade others to support them. However, disparities in participation levels can lead to misperceptions and cause analysts to misjudge the support for each side. For example, current models usually rely on content production and overlook a vast majority of civically engaged users who passively consume information. These “silent users” can significantly impact the democratic process despite being less vocal. Accounting for the stances of this silent majority is critical to improving our reliance on SM to understand and measure social phenomena. Thus, this study proposes and evaluates a new approach for silent users’ stance prediction based on collaborative filtering and Graph Convolutional Networks, which exploits multiple relationships between users and topics. Furthermore, our method allows us to describe users with different stances and online behaviors. We demonstrate its validity using real-world datasets from two related political events. Specifically, we examine user attitudes leading to the Chilean constitutional referendums in 2020 and 2022 through extensive Twitter datasets. In both datasets, our model outperforms the baselines by over 9% at the edge- and the user level. Thus, our method offers an improvement in effectively quantifying the support and creating a multidimensional understanding of social discussions on SM platforms, especially during polarizing events.

AB - Social Media (SM) has become a popular medium for individuals to share their opinions on various topics, including politics, social issues, and daily affairs. During controversial events such as political elections, active users often proclaim their stance and try to persuade others to support them. However, disparities in participation levels can lead to misperceptions and cause analysts to misjudge the support for each side. For example, current models usually rely on content production and overlook a vast majority of civically engaged users who passively consume information. These “silent users” can significantly impact the democratic process despite being less vocal. Accounting for the stances of this silent majority is critical to improving our reliance on SM to understand and measure social phenomena. Thus, this study proposes and evaluates a new approach for silent users’ stance prediction based on collaborative filtering and Graph Convolutional Networks, which exploits multiple relationships between users and topics. Furthermore, our method allows us to describe users with different stances and online behaviors. We demonstrate its validity using real-world datasets from two related political events. Specifically, we examine user attitudes leading to the Chilean constitutional referendums in 2020 and 2022 through extensive Twitter datasets. In both datasets, our model outperforms the baselines by over 9% at the edge- and the user level. Thus, our method offers an improvement in effectively quantifying the support and creating a multidimensional understanding of social discussions on SM platforms, especially during polarizing events.

KW - Collaborative filtering

KW - Graph convolutional networks

KW - Recommendation system

KW - Stance prediction

UR - http://www.scopus.com/inward/record.url?scp=85189649667&partnerID=8YFLogxK

U2 - 10.1140/epjds/s13688-024-00469-y

DO - 10.1140/epjds/s13688-024-00469-y

M3 - Article

AN - SCOPUS:85189649667

VL - 13

JO - EPJ Data Science

JF - EPJ Data Science

SN - 2193-1127

IS - 1

M1 - 28

ER -