NEGATIVE RHETORIC AND ENGAGEMENT ON FACEBOOK

Emotions are woven into politics through the everyday norms of political communication, which gives political elites the opportunity to engage voters and build up or maintain support (Gadarian and Van der Vort Reference Gadarian and Van der Vort2018). Campaigns consistently facilitate an array of reactions from voters, but negative rhetoric has become more prominent—both in practice and in scholarship (Brader Reference Brader2006; Gross and Johnson Reference Gross and Johnson2016; Lodge and Taber Reference Lodge and Taber2005; Marcus Reference Marcus, Huddy, Sears and Levy2003; Valentino et al. Reference Valentino, Brader, Groenendyk, Gregorowicz and Hutchings2011). Affective Intelligence Theory suggests that voter anger or anxiety can be activated by candidates seeking voters’ attention, and candidates seeking to change the status quo will turn to negativity to gain an electoral advantage (Brader Reference Brader2006; Marcus Reference Marcus, Huddy, Sears and Levy2003). Negative appeals in the form of visuals also are used strategically to persuade voters; photographs and videos, for example, are becoming a mainstay of Facebook persuasive content (Albertson, Dun, and Gadarian Reference Albertson, Dun, Gadarian, Suhay, Grofman and Trechsel2020).

In addition to persuasiveness, evidence from field experiments reveal that negative sentiment can influence electoral participation and mobilization (Freedman and Goldstein Reference Freedman and Goldstein1999; Panagopoulos Reference Panagopoulos2011), although this activation often is conditional on timing, context, and individual attributes of a candidate (Krupnikov and Bauer Reference Krupnikov and Bauer2014). This suggests that negative information may be an important antecedent to participation, given that voters weigh it more heavily than positive information (Garramone et al. Reference Garramone, Atkin, Pinkleton and Cole1990), and negativity may be more likely to break through voters’ persistent inattention (Soroka Reference Soroka2014). Beyond motivating action, negativity also can produce more emotional or affective responses that stay with voters over time and induce content sharing across social media (Berger and Milkman Reference Berger and Milkman2012). This emotional activation is particularly poignant when linked with social media networks designed to capitalize on the weak connections among users.

Congressional candidates increasingly are using social media as voter outreach tools and relying on Facebook and Google for their strategic communications (Kreiss and McGregor Reference Kreiss and McGregor2019). Data from the 2008 elections show that politicians’ Facebook use strongly impacted the growth of political participation (Bode et al. Reference Bode, Vraga, Borah and Shah2014; Borah Reference Borah2016). Tweets from political parties during the 2013 Italian elections suggest that emotional appeals, particularly negative ones, had positive impacts on vote intentions (Ceron and d’Adda Reference Ceron and d’Adda2016). Facebook gives users a way to make appeals, both logical and emotional, to other users (Bazarova and Choi Reference Bazarova and Choi2014). Although the true nature of that interactivity is debatable, the potential for that engagement continues to draw candidates and followers together on social media. Candidates in the United States and abroad use Facebook to select information for voters and target messages based on their audience (Bond Reference Bond2017; Waterson Reference Waterson2017). On Facebook, that audience is part of a self-selected network where information can be curated further for a unique audience. This highlights the importance of considering differing levels of positive and negative emotions across platforms that support variable levels of strong and weak ties among users (Bode et al. Reference Bode, Lassen, Kim, Shah, Fowler, Ridout and Franz2016; Waterloo et al. Reference Waterloo, Baumgartner, Peter and Valkenburg2018).

SYSTEMATIC VARIATION IN POLITICAL RHETORIC AND ENGAGEMENT

The October 2021 release of the “Facebook Files” highlights the power of social media engagement by detailing, among other issues, the company’s efforts to modify the tone of the platform—creating a ranking system to increase interactions and shape users’ News Feeds.Footnote ² Under this system, “likes” received 1 point each, reactions and shares received 5 points each, and comments received 15 or 30 points (depending on how “significant” they were) (Hagey and Horwitz Reference Hagey and Horwitz2021). The more points that a post received, the wider it spread—meaning that for political candidates, those engagements were linked directly to message spread. The intricacies of Facebook’s News Feed algorithm illustrate the importance of understanding what fuels engagement and the type of posts that candidates can use to build support because norms reinforced on Facebook can spur user mobilization (Haenschen Reference Haenschen2016).

The tone that candidates use online and the responses from users have implications beyond only the political “echo chamber,” and that tone—even indirectly—shapes the political climate for campaigns. Because negativity has become a dominant feature of social media campaigns, the authors examined the impact of that negative rhetoric to elicit varying responses among users. To do so, we explored whether negative appeals are associated with more user engagement and if those user responses match the negative appeals by candidates in terms of sentiment. We expected that the negativity politicians share on Facebook is likely to spur engagement through likes and comments but, even more important for campaigns, that content is more likely to be shared. Additionally, the impact of that negativity is not only spread but also the specific types of reactions that users give to those posts, which fuels more negative reactions that shape the broader dialogue on Facebook and gives politicians agenda-setting power. Following are our negative-engagement hypotheses:

• • H1: Candidates are more likely to receive higher levels of public engagement (i.e., likes, shares, and comments) on Facebook posts with more negative sentiment.

• • H2: Candidates are more likely to receive more negative reactions on Facebook posts with more negative sentiment.

DATA AND METHODS

To test our hypotheses, we used CrowdTangle to collect all Facebook posts from the campaign accounts of 696Footnote ³ candidates who ran for Congress in the US House of Representatives in 2020, from January 3, 2019, to November 13, 2020 (N = 279,580 posts) (Macdonald, Russell, and Hua Reference Macdonald, Russell and Hua2022). CrowdTangleFootnote ⁴ allowed us to track public content as well as interactions with that content (i.e., reactions, likes, comments, and shares). Although engagement data are imperfect and do not provide the entire picture of how candidates and users interact on social media, they do reveal important and valuable insights into how candidates use their Facebook platforms and how certain content performs on their pages.

Table 1 summarizes the statistics for post frequency by all candidates. Candidates were coded as an incumbent if they were elected in the 2018 midterms or in a special election before November 2020 and coded as a non-incumbent otherwise. Of the total candidates in our data, 347 were incumbents and 349 were challengers. We used scores from the 2020 Cook Report to label each district as competitive or non-competitive.Footnote ⁵ If the Cook Report labeled a district as likely Democrat/Republican, to lean Democrat/Republican, or a toss-up as of November 2, 2020, we considered the district to be competitive. Of the 435 districts in 2020, 89 were considered competitive using this benchmark.

Table 1 Facebook Post Frequency by Candidate Type

The average candidate posted on Facebook 402 times (see table 1). We also observed candidate-level variation among our variables: the average non-incumbent posts more than the average incumbent; Democrats more than Republicans; women more than men; and those running in competitive districts more than those running in non-competitive districts, on average. Figures A1–A4 in the online appendix plot tweets over time for these groups.

DO NEGATIVE APPEALS RECEIVE MORE ENGAGEMENT?

Individuals pay more attention to negative information (Soroka Reference Soroka2012); therefore, how candidates integrate negative words into their Facebook posts has implications for engagement. We observed a high prevalence of posts with negative words in our data, which illustrates how candidates are regularly integrating negativity into their campaign messaging. To measure the use of negative sentiment and label negative posts, we used a dictionary approach. We measured candidates’ reliance on emotional appeals with an emphasis on negative sentiment.

The dictionary used for our analyses was derived from the NRC Word–Emotion Association Lexicon (Mohammad and Turney Reference Mohammad and Turney2013).Footnote ⁶ We labeled Facebook posts as negative using a keyword dictionary of words associated with negative valence. Previous work manually evaluated all words in the NRC negative-sentiment dictionary (k = 3,342 unigrams) to determine whether each word reasonably could be used in the political context of congressional campaigns to signal negativity (Hua and Macdonald Reference Hua and Macdonald2020). This modified dictionary was used to label posts by calculating a frequency score of the number of emotion-associated words in each post.Footnote ⁷ For example, a Facebook post with a score of 13 means that 13 words from the modified negative-language dictionary appeared in that post.Footnote ⁸

Relative to other social media platforms, Facebook offers greater variation in the types of responses that users can make to posts. Alongside likes, shares, and comments (e.g., as on Twitter), Facebook users also can react emotionally to content with “love,” “wow,” “haha,” “sad,” “angry,” and “care.” We considered more likes,Footnote ⁹ shares, and comments to be indicative of a higher public response and engagement. Additionally, “angry” and “sad” reactions were categorized as negative responses in our analysis.Footnote ¹⁰

We estimated ordinary least squares (OLS) regression models for different types of engagement. The first three columns in table 2 test Hypothesis 1 and consider likes, comments, and shares. For each of the three models, the dependent variable was the average number of each engagement per candidate-week-post type, respectively. “Post type,” or multimedia, was defined as whether a post is a multimedia post (i.e., video, photograph, or live) or otherwise (i.e., link or status). The explanatory variable of interest was the average proportion of negative words (i.e., “proportion negative”). To calculate this, we divided the average number of negative words by the average number of words per post for each candidate-week-post type, considering the fact that there can be a wide range of length in Facebook posts. We also included controls for candidate gender, party, incumbency, district competitiveness, race, total number of Facebook posts, and total average page likes. To account for variation during the course of the campaign and differences among states, we included week and state fixed effects.

Table 2 OLS Regressions of Proportion of Average Negative Words per Post (by Candidate-Week-Post Type), with Controls

Notes: Degrees of freedom = 154, 66,595. *p < 0.1; **p < 0.05; ***p < 0.01. Models include week and state fixed effects.

We found support for Hypothesis 1 in table 2—that is, the higher the proportion of negative words, the higher average likes, shares, and comments that candidate posts received. These results suggest that choosing to incorporate more negative rhetoric into their Facebook posts may enable candidates to achieve a greater spread of their message on the platform—particularly through shares—which is a central goal of campaigns. We observed this negativity bias across different social media platforms and among elected officials in office (Gervais, Evans, and Russell Reference Gervais, Evans, Russell, Foreman, Godwin and Wilson2020; Russell Reference Russell2018). Negativity is one of the strongest and most consistent predictors of reactions. Candidates who want their content to spread virally are more likely to have it shared if the post contains a higher proportion of negativity. However, we also observed meaningful differences across party and gender that could moderate some of those effects similar to prior research (Gervais, Evans, and Russell Reference Gervais, Evans, Russell, Foreman, Godwin and Wilson2020; Macdonald et al. Reference Macdonald, Brown, Tucker and Nagler2022).Footnote ¹¹

To test Hypothesis 2, we estimated OLS models of the same format as described previously. We predicted that candidates receive more negative reactions when they use more negative rhetoric in their posts, suggesting that elite rhetoric can condition the type of response. The results are shown in the last two columns of table 2. The main independent variable “proportion negative” was used again, as well as the same control variables. In these models, the dependent variables are now the average “sad” and “angry” reactions received by candidate-week-post type. We found strong support for our expectations. When candidates used higher proportions of negative sentiment in their posts, they received more “sad” and “angry” reactions.Footnote ¹² The ability of candidates to use tone to elicit different types of reactions from users provides additional clarity on the power of Facebook engagement and the discretion that candidates have to shape the political climate.

To account for concerns that we were sensing a different dynamic than negativity, other analyses are in the online appendix. Appendix table A5 shows the same analysis as in table 2, except that the dependent variable is the proportion of positive words. We found very different results: all of the coefficients for the proportion of positive words were negative and the coefficients for comments and angry reactions were statistically significant.Footnote ¹³ Candidates who used more positive language, on average, received less engagement than those who used more negative language.Footnote ¹⁴