Mining the Cause of Political Decision-Making from Social Media: A Case Study of COVID -19 Policies across the US States (2021)

Abstract: Mining the causes of political decision-making is an active research area in the field of political science. In the past, most studies have focused on long-term policies that are collected over several decades of time, and have primarily relied on surveys as the main source of predictors. However, the recent COVID-19 pandemic has given rise to a new political phenomenon, where political decision-making consists of frequent short-term decisions, all on the same controlled topic—the pandemic. In this paper, we focus on the question of how public opinion influences policy decisions, while controlling for confounders such as COVID-19 case increases or unemployment rates. Using a dataset consisting of Twitter data from the 50 US states, we classify the sentiments toward governors of each state, and conduct controlled studies and comparisons. Based on the compiled samples of sentiments, policies, and confounders, we conduct causal inference to discover trends in political decision-making across different states.

## introduction policy responsiveness is the study of the factors that policies respond to @xcite . one major direction is that politicians tend to make policies that align with the expectations of their constituents, in order to run successful re-election in the next term @xcite . an overview of existing studies on policy responsiveness reveals several patterns, summarized in we present one of the first efforts to address policy responsiveness for short-term policies, namely the causal impact of public twitter sentiments on political decision-making. this is distinct from existing studies on covid policies that mostly explore the impact of policies, such as predicting public compliance @xcite @xcite @xcite . specifically, since governors have legislative powers through executive orders, we focus our study on each state governor's decisions and how public opinion towards the governor impacts their decisions. for example, governors that optimize short-term public opinion are more likely to re-open the state even when case numbers are still high. our workflow is illustrated in figure 1 . we start by collecting 10.4m governor-targeted covid tweets, which we annotate for sentiment with a bert-based classifier. next, we annotate 838 social distancing policies and collect data on ten potential confounders such as average daily case increases or unemployment rates. finally, we conduct multiple analyses on the causal effect of twitter sentiment on covid policies. for interpretability, we first use a multivariate linear regression to identify correlations of sentiments and policies, in addition to considering all the confounders. we also use do-calculus @xcite to quantify the causal impact of twitter sentiment on policies. we also conduct cross-state comparisons, cross-time period analysis, and multiple other analyses. the main contributions of our work are as follows. first, we compile a dataset of public opinion targeted at governors of the 50 us states with 10.4m tweets. second, we annotate a dataset of 838 covid policy changes of all 50 states, along with data of ten confounders of each state. third, we conduct regression analyses and causal analyses on the effect of twitter sentiment on policies. finally, we implement additional fine-grained analyses such as cross-state comparisons, cross-time period analysis, and multiple other analyses. ## governor-targeted public opinion to investigate the causality between public opinion and each state governor's policy decisions, we first describe how we mine public opinion in this section; we then describe the process we use to collect policies and other confounders in section 4. we collect governor-targeted public opinion in two steps: (1) retrieve governor-related covid tweets (section 3.1), and (2) train a sentiment classification model for the covid tweets and compile sentiments towards governors (section 3.2). ## collection of policies and confounders we focus on state-wide social distancing policies, and collect 838 social distancing policies from 50 states over the period january 2020 -april 2021 (described in section 4.1). since we want to focus on the causal effect of public sentiment on policy, we must control for possible confounding factors. in particular, case numbers and unemployment rates are potentially the most important confounders, the collection of which is introduced in section 4.2. in addition, we also collect eight other potential confounders suggested by political science experts (described in section 4.3). the collection process is illustrated in figure 1 . ## mining decisive factors of covid policies since we are interested in discovering the key factors that changes the decisions of policy-makers, we focus on the change of policies (e.g., changing from complete close down to reopening k-12 schools) rather than absolute values of the policy strictness. for each policy in state s on date t, we calculate the change ∆policy as the difference of this policy from the previous policy that was issued. since sentiment may change rapidly and many policies are updated frequently during covid, for each policy change ∆policy, we focus on the average sentiment over the time span (t -∆t, t) from ∆t days prior to the policy date t. here, we set ∆@xmath0, e.g., the 14-day notification rate. when building the policy prediction model, we also need to account for confounders. for the confounders, most are static over time for a given state, except for the daily case increases and the unemployment rates that change over time, for which we take the average over the 14-day time span. based on the data above, we seek to answer the following questions: (q1) what variables are indicative of policy changes?, and (q2) what causal impact does sentiment have on the policies? ## additional discussions fine-grained opinions behind the sentiments. to further interpret why positive tweets usually lead to stricter social distancing policies (and negative tweets lead to looser policies), we look into the correlation of twitter sentiment and the user's opinion towards social distancing policies. note that usually it is not easy to directly get an unsupervised intent classifier on covid specific tweets. hence, we ask the annotators to classify the opinion on social distancing for the 500 tweets in our test set as supportive, against, and not related to social distancing. among the tweets about social distancing with positive sentiment, 95.13% support social distancing. among the tweets about social distancing with negative sentiment, 69.38% are against social distancing and ask for the reopening of the state. ## conclusion in this paper, we conducted multi-faceted analyses on the causal impact of twitter sentiment on covid policies in the 50 us states. to enable our study, we compile a large dataset of over 10 million governor-targeted covid tweets, we annotate 838 state-level policies, and we collect data ten potential confounders such as daily covid cases and unemployment rates. we use a multivariate linear regression and do-calculus to quantify both the correlation of twitter sentiment as well as its causal impact on policies, in the presence of other confounders. to our knowledge, this is one of the first studies to utilize massive social media data on crisis policy responsiveness, and lays the foundation for future work at the intersection of nlp and policy analyses. our code and data are publicly available at https://github.com/zhijing-jin/ covid-twitter-and-policy .