Abstract
Purpose
The purpose of this paper is to understand why some US Senators have more low-quality followers than others and the potential impact of low-quality followers on understanding constituent preferences.
Design/methodology/approach
For each US Senator, data on Twitter followers was matched with demographic characteristics proven to influence behavior. An OLS regression model evaluated why some Senators attract more low-quality followers than others. Then, observations on the impact of low-quality followers were discussed along with potential effects on information gathering and constituent representation.
Findings
This study finds that total followers, ideology and length of time on Twitter are all significant predictors of whether a Senator might attract low-quality followers. Low-quality followers can have wide-ranging implications on Senator’s use of social media data to represent constituents and develop public policy.
Research limitations/implications
The data set only includes Senators from the 115th Congress (2017–2018). As such, future research could expand the data to include additional Senators or members of the House of Representatives.
Practical implications
Information is essential in any decision-making environment, including legislatures. Understanding why some users, particularly public opinion leaders, attract more low-quality social media followers could help decision-makers better understand where information is coming from and how they might choose to evaluates its content.
Social implications
This study finds two practical implications for public opinion leaders, including Senators. First, accounts must be actively monitored to identify and weed-out low-quality followers. Second, users need to be wary of disinformation and misinformation and they need to develop strategies to identify and eliminate it from the collection of follower preferences.
Originality/value
This study uses a unique data set to understand why some Senators have more low-quality followers than others and the impact on information gathering. Other previous studies have not addressed this issue in the context of governmental decision-making or constituent representation.
Subject
Information Systems and Management,Computer Science Applications,Public Administration
Reference100 articles.
1. Congressional receptiveness to constituent contacts through social media,2017
2. Badly evolved? Exploring long-surviving suspicious users on twitter,2017
3. Atlantic Council (2021), “Disinformation”, available at: https://atlanticcouncil.org/issue/disinformation
4. Who leads? who follows? measuring issue attention and agenda setting by legislators and the mass public using social media data;American Political Science Review,2019
5. Representing the preferences of donors, partisans, and voters in the US senate;Public Opinion Quarterly,2016