First Monday

Angry sharing: Exploring the influence of Facebook reactions on political post sharing by Anders Olof Larsson



Abstract
Political campaigning is increasingly undertaken by means of social media such as Facebook. Indeed, having content spread on Facebook by means of users sharing the posts of political parties and politicians has been pointed to as highly important. Based on suggestions from previous research, we investigate the relationship between shares and other Facebook post engagement opportunities such as comments and reactions. Formulating a series of hypotheses and research questions, a longitudinal study of the Norwegian political context is presented. The main findings show that while the Angry and Sad reactions emerge as positively related to the number of shares, Love and Care variations do not exhibit such clear relationships. Reviewing the findings in the light of previous scholarship, the results are interpreted as significative of the importance of negative emotions for online success.

Contents

Introduction
Engaging with political content on Facebook
Method
Results
Discussion and conclusion

 


 

Introduction

February 2016 saw the Facebook engagement varieties of liking, commenting and sharing complemented to also include reactions — an expansion of the like button to feature a series of different emoticons expressing, among other emotions, sadness, anger and love (Krug, 2016). While the specific, situated meaning such digital traces undoubtedly rests with the engaging user (Lomborg and Bechmann, 2014), recent research has nevertheless suggested that post sentiment relates to different types of reactions received by the post (Eberl, et al., 2020). In other words, these “emotionally labeled emojis” [1] actually appear to say something about the emotions expressed by Facebook users in relation to the content provided.

This study looks at the relationship between different types of Facebook reactions and the number of shares received by posts made by Norwegian political actors during three elections. Given the especially important role of shares to boost post visibility and popularity (Bene, 2018; Bene, et al., 2022; Heidenreich, et al., 2024), we conceptualize the number of shares as our dependent variable and investigate the relationship between Facebook reactions and sharing. Indeed, focusing on emotions such as those possible to express by means of Facebook reactions has been identified as important to gain further insights into political as well as related spheres (Jacobs, et al., 2020; Papacharissi, 2015; Wahl-Jorgensen, 2019). Remembering that “the study of political user engagement is a growing field” [2], we aim to shed further light on how user engagement is related to post popularity — understood here as the number of shares received by a post. Focusing on Facebook page post data from Norwegian political actors during three recent election years — 2017, 2019 and 2021 — this study adopts a descriptive, diachronic approach and traces these relationships over time (as suggested by Bruns and Moon, 2018; Esser and Pfetsch, 2020; Helmond, et al., 2019; van Vliet, et al., 2020). Furthermore, the study details a non-U.S. context (as suggested by Chadwick and Stromer-Galley, 2016; Dimitrova, et al., 2003; Tromble, 2018) and combines quantitative and qualitative approaches (inspired by Bruns and Moon, 2018; Bruns, et al., 2017; Mahrt and Scharkow, 2013). Finally, this study takes the influx of populism into account — comparing activity undertaken in relation to populist actors with that performed in relation to their non-populist competitors.

Our main findings suggest that while the Angry and Sad varieties of reactions emerge as positively related to the number of shares per post, the Love and Care variations do not exhibit such clear relationships. Reviewing our findings in the light of previous scholarship, we argue that our results speak to the importance of negative emotions for online success. Based on the efforts presented in the paper, ideas for future research projects are suggested.

 

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Engaging with political content on Facebook

With the online platforms such as social media, the media logics suggested by Altheide and Snow (1979) have been complemented with a multitude of other theoretical conceptualizations accounting for the influx of platforms such as Facebook (e.g., Klinger and Svensson, 2015; Van Dijck and Poell, 2013). Common for these perspectives is the importance of audience engagement with content in order for it to reach more users. Content should be prepared in such a way that is easily shared by users (Berger and Milkman, 2012; Nahon and Hemsley, 2013), strengthening the claim that it “is popularity and not news values or other professional criteria that determine the relevance and reach of a message” [3]. The resulting dependency on user engagement and its potential to boost the “popularity of people, things, or ideas” [4] carries with it a necessity for political actors to amend their campaigning efforts accordingly. Indeed, Bene [5] pointed out that while such efforts were previously largely constructed to affect citizen cognitions and affections, political campaigning must now also take the social aspect of communication into account. Certainly, “[p]eople must show each other how they can appropriate, shape, and share themes” [6], allowing their content to be “amplified” (Zhang, et al., 2017) and made visible to Facebook users. Thus, while increasing the number of followers to one’s Facebook Page is important for success on the platform (Klinger, 2013; Samuel-Azran, et al., 2015), simply amassing followers will not be enough for online prowess if you do not simultaneously succeed in activating the engagement of those followers. Out of the different ways that followers can engage with Facebook Pages, the sharing variety has been pointed to as most important.

The importance of shares

While sharing appears to have become less important in the self-presentations of social media platforms (John, 2024, 2013), it nevertheless still a key metric for political actors or anyone seeking to spread their messages on the platform under scrutiny here. By getting followers to share your posts further, you can reach beyond your self-selecting (Messing and Westwood, 2012), primary (Jost, 2023) audiences, securing exposure to what has been referred to as incidental (Heidenreich, et al., 2024; Karnowski, et al., 2017) audiences — i.e.?, those users who do not follow your Page, but who are Facebook friends with one such sharing user. As “sharing content leaves visible traces on users’ own timeline” [7], this particular variety of engagement allows candidates to “preach through the converted” (Vissers, 2009) rather than “preaching to the converted” (Norris, 2003) — a flow of communication somewhat reminiscent of the two-step model of communication (Lazarsfeld, et al., 2021). Given the importance of shares, we conceptualize this particular metric as our dependent variable. We further suggest a series of independent variables in order to trace differences and tendencies in sharing patterns across the three elections as mentioned earlier. Among other things, we are interested in the relationship between shares and the different types of reactions offered by Facebook. Drawing on this suggestion as well as incorporating suggestions from previous research in order, we formulate a series of hypotheses and research questions regarding these relationships.

Exploring the influence of likes, comments and reactions

First, for likes, this form of engagement has been pointed to as “a lower-level form of participation” [8], supposedly requiring a smaller amount cognitive effort compared to other varieties of engagement (Eberl, et al., 2020). As the act of liking a post has been described as “click speech” [9], “lightweight signals” [10] and “popularity cues” [11], the ‘like’ must be considered as notoriously difficult to pin down when it comes to its meaning (e.g., Koc-Michalska, et al., 2021; Ozanne, et al., 2017). Indeed, Hayes and co-authors (2016) demonstrated how likes were used in relation to Facebook content ranging from the celebratory to the mournful, referring to likes as “paralinguistic digital affordances” [12] — a concept seemingly similar to that of phatic communication (Malinowski, 1972). While they do not suggest a precise emotion or meaning, we expect likes to function as a more general predictor of post popularity. With this in mind, we formulate our first hypothesis as follows.

H1: Likes and shares will be positively correlated to each other.

Second, for comments, a series of scholars suggest that this form of engagement — regardless of platform or service — is probably the most discussed as well as the most extensively researched (e.g., Eberl, et al., 2020; Ziegele, et al., 2017). Much as for likes, comments — given their open-ended nature — do not by their nature imply support or agreement. While earlier work tended to point to engagement in this regard as an almost universal good which would enable “unparalleled forms of deliberation while also strengthening democratic processes” [13], potentially revitalizing a Habermasian public sphere (Dahlberg, 2001; Papacharissi, 2002), later insights have pointed to the problematic sides of comments. Quandt (2018) refers to this comparably latter tendency as “dark participation”, pointing to behaviors such as incivility and trolling in a series of different online contexts. Such difficulties associated with handling and indeed responding to comments (Koc-Michalska, et al., 2021) might lead to the need to moderate, hide or even delete certain offerings (Kalsnes and Ihlebæk, 2021). Regardless of what is expressed in these utterances, comments will nevertheless serve to further amplify the content commented upon (Zhang, et al., 2017). However, as previous research has suggested that the often-conflicting nature of politics stand in the way of users engaging with political content (Vraga, et al., 2015), we might expect higher numbers of comments as an indicator of topic controversy and potential problematic or indeed dark participation. While we can also point to the difficulties of getting users to share such controversial content (Costera Meijer and Groot Kormelink, 2015), it does not seem entirely unlikely that an especially controversial post can go “viral” thereby earning many shares and comments (e.g., Larsson, 2018). With the discrepancies discussed above in mind, our first research question reads as follows:

RQ1: What is the relationship between comments and shares?

Third, for reactions, these emoji-like varieties of the aforementioned like button were introduced by Facebook after platform representatives supposedly heard from users that wanted “a way to express empathy” (Chowdhry, 2017). As mentioned earlier, while we should not view an Angry or a Love reaction as directly parallel to the intent or mood of the user leaving such digital traces, empirical research nonetheless suggests that such a connection might exist. Studying the 2017 Austrian election, Eberl and co-authors (2020) combined automated content analysis of political actor Facebook posts and voter survey data. Among their findings, the authors report that “the more positive/negative the language of a Facebook post, the more ‘Love’/‘Angry’ Reactions that post will receive” [14], suggesting a link between post content and audience reaction. While the authors rightfully point out that uses of Reactions that are unrelated to post contents are certainly possible, their findings nevertheless suggested a link of interest for the study presented in this paper.

Indeed, emotions — such as those supposedly expressed by means of Facebook Reactions — play an important role in political communication processes. In a more general sense, emotional expressions have been shown to affect citizen involvement with political campaigns (Namkoong, et al., 2012) as well as messages sent by political actors (Brader, 2005). Surely, previous scholarship suggests that the study of emotions in this regard has grown more and more important in several disciplines (Marcus, 2023). Thus, we might expect “political implications of a number of discrete emotional reactions” [15] such as those represented by the emojis used as reactions on Facebook. Perhaps in line with the tendencies of “dark participation” mentioned earlier, comparably recent research has pointed to the tendency for negativity to increase user engagement (Bene, et al., 2022; Larsson, 2018; Reuning, et al., 2022). While some have pointed to the influence of positive content on Facebook sharing (Heidenreich, et al., 2024), the bulk of scholarship into these matters appear to suggest negativity as a stronger driver of the specified measurement than positivity. Our third hypothesis is formulated accordingly.

H2: Negative reactions (Sad, Angry) will drive sharing to higher extents than positive reactions (Love, Care).

While the four types of Reactions dealt with in H3 can be defined as negative or positive (with certain caveats as discussed previously), the remaining Wow and Haha varieties are not as easily defined. Adopting an exploratory approach, they will nevertheless be entered into our analysis to see if any patterns emerge that can serve to more fully understand how popularity plays out on Facebook.

Beyond these different forms of engagement, we can also trace other aspects — specifically, aspects pertaining to the political actors themselves — that are likely to exert influence on numbers of shares per post. As mentioned above, previous research has pointed clearly to the tendency for populist actors to be more successful online than their non-populist competitors (e.g., Ceccobelli, et al., 2020; Gil de Zúñiga, et al., 2020; Jacobs, et al., 2020) — especially, it would seem on the platform under scrutiny (Larsson, 2022). With this in mind, we expect similar patterns of populists gaining shares to higher degrees to be evident also in the Norwegian context studied here. Second, as previous research has indicated that individual politicians tend to fare better online in terms of receiving engagement such as shares (Jacobs, et al., 2020; Karlsen and Enjolras, 2016; Larsson, 2020), we expect similar results to emerge from the empirical material scrutinized here. Based on the above, our forth and fifth hypotheses are phrased as follows:

H3: Populists will receive more shares than non-populists.
H4: Individual politicians will receive more shares than parties.

 

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Method

Data from the Facebook Pages of what are often considered as the main Norwegian political actors were collected by means of CrowdTangle, a service owned by Meta that allows for the archiving of Page post text and associated metadata (Fraser, 2020; Larsson, 2023). By political actors, we mean the nine parties, their respective party leaders and key politicians holding representation in parliament at the time of the latest election included in the study — 2021. Table 1 provides an overview of these parties and politicians.

 

Table 1: Overview of included Norwegian parties and politicians included in this study
Party
(Politician)
AbbreviationIdeological alignment
PopuList classification
Vote percentage in 2017Vote percentage in 2019Vote percentage in 2021
Arbeiderpartiet
(Jonas Gahr Støre)
ApSocial Democrat27.424.826.3
Høyre
(Erna Solberg)
HConservative2520.120.4
Senterpartiet
(Trygve Slagsvold Vedum)
SpAgrarian10.314.113.5
Fremskrittspartet
(Siv Jensen, Sylvi Listhaug)
FrpBorderline far right, populist15.28.211.6
Sosialistisk Venstreparti
(Audun Lysbakken)
SVBorderline far left66.17.6
Rødt
(Bjørnar Moxnes)
RFar-left, borderline populist2.43.84.7
Venstre
(Trine Skei Grande 2017-2019, Guri Melby 2021)
VLiberal4.43.94.6
Miljøpartiet De Grønne
(Une Bastholm)
MDGEnvironmental3.26.83.9
Kristelig Folkeparti
(Knut Arild Hareide 2017–2019, Kjell Ingolf Ropstad 2021)
KrfChristian Democratic4.243.8

 

Key politicians were included in order to be able to investigate those parliamentary representatives who had been pointed out by previous research as especially interesting with regards to their social media activities. Specifically, Sylvi Listhaug stands out in particular (Larsson, 2020) and was therefore added to this study. Current party leader and former representative of the right-wing populist Progress Party (Fremskrittspartiet in Table 1), Listhaug’s Facebook Page was included beside the Page operated by the party itself as well as the Page managed by the former leader, Siv Jensen. Following the example of previous work regarding populist parties, these three identified actors were classified accordingly (Rooduijn, et al., 2019) in our data set.

Posts were collected that had been provided by each party or politician during a one-month period leading up to each respective election day. With the first election — a national election — held on 2017-09-11, data was collected from 2017-08-11, resulting in 1927 collected posts. The next election — a regional election — was held on 2019-09-09 and subsequently data was collected from 2019-08-09, yielding a total of 2953 posts. Finally, the 2021 national election was held on 2021-09-13. With data collection thus reaching back to 2021-08-13, a total of 2,568 posts were collected. Dummy variables were created for populist actors (measured as 0 = non-populist and 1 = populist) as well as for whether posts had been made available by parties (coded as 0) or by individual politicians (coded as 1).

In order to investigate the proposed hypotheses, a series of negative binomial regression models were used. Given the count data nature of our dependent variable across the three years studied — N of shares received per post — the specified variety of regression was deemed suitable (inspired by Hilbe, 2014; Jacobs, et al., 2020; Koc-Michalska, et al., 2021; Trilling, et al., 2017). Beyond addressing the proposed hypotheses, the regression results were also used to inform a more qualitative, visual approach to analysis, the aim of which was to provide further insights into the characteristics of posts that had become especially popular in terms of sharing.

 

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Results

Table 2 presents the results of the year-by-year negative binomial regression analyses.

 

Table 2: Negative binomial regression analyses, presented per year.
Dependent variable = N of shares.
 201720192021
exp(B)SEpexp(B)SEpexp(B)SEp
Likes1.001.0001<.0011.002.0001<.0011.001.0001<.001
Comments1.001.0003<.001.999.0002<.0011.000.0001.615
Love1.001.0006.2071.000.0003.5791.000.0002.106
Care1.499.1747.0211.000.0003.477
Wow1.017.0040<.0011.001.0029.021.999.0013.582
Haha1.002.0010.1401.003.0007.0021.000.0003.132
Sad1.003.0014.0421.005.0007<.0011.001.0003.019
Angry1.003.0007.1011.002.0004<.0011.002.0003<.001
Populist
(0–1)
1.098.0800.007.846.0611.0061.301.0591<.001
Politician
(0–1)
1.369.0477<.0011.562.0388<.0011.320.0424<.001
Log-likelihood-9443.9-13744.8-12316.7
Likelihood2664.72251.91267.5
Ratio(.000)(.000)(.000)
AIC18907.927511.524655.4
N1,9272,9532,568

 

Our first hypothesis suggested that likes would be positively related to our dependent variable. As can be seen in Table 2, results across all years indicate support for the hypothesis. Nevertheless, as suggested by the reported incidence rate ratio, the influence emerges as rather weak for each year, perhaps mirroring the low-effort nature of post liking as discussed earlier.

Results emerge as a bit more varied when we move on to assess our first research question, investigating the relationship between comments and shares. For 2017, the results suggest a positive relationship, while the 2019 data points in the opposite direction. For 2021, comments did not emerge as a significant predictor. With these mixed results at hand, it would appear that further work is needed to gauge the relationship between the comment and share varieties of engagement.

Moving on, our second hypothesis suggested that negative reactions (Sad, Angry) would drive sharing to higher extents than positive reactions (Love, Care). The results presented in Table 2 largely supports the hypothesis — with one caveat. Specifically, while Care emerged as a positive, significant predictor in 2017, neither Love nor Care were similarly significant during the other studied time periods. By contrast, Sad and Angry emerged as positive predictors throughout all three years, reaching significance in most cases. While the bulk of these measurements as reported in Table 2 must be considered diminutive, these slight differences between positive and negative reactions nevertheless suggest a dissimilarity in relations between different reactions and our dependent variable. While we did not formulate any hypothesis in relation to the Wow and Haha varieties of reactions, the results here appear as varied throughout the time periods studied, with both predictors ending up as non-significant during our final year.

Our third hypothesis suggested that populist actors would be more successful with regards to receiving shares than their non-populist competitors. While this appeared to be the case for the two national elections under scrutiny (2017 and 2021), the results suggested an opposite tendency during the regional election of 2019. While data from two out of three data points emerged in line with our expectations, the third hypothesis cannot be considered as supported. Finally, all three studied years provide support for our fourth hypothesis — individual politicians do indeed appear to provide more “shareworthy” [16] content than the parties to which they belong.

Given the uncovered important role of Sad and Angry reactions in relation to post sharing, we complement the regression analyses with a series of visualizations. Specifically, the following visualizations are based on the relationships between sharing, Sad and Angry for each year. In the figures, each post is represented by a node, where node size represents the number of shares, placement on vertical axis indicates the number of Angry reactions and horizontal placement is representative of the number of Sad reactions. Node shape, then, shows if the post itself was provided by a politician or by a party — round nodes for parties, square nodes for politicians. Node color represent type of political actor — orange nodes represent posts by populists, while blue nodes indicate that the corresponding post was made by a non-populist actor. We present these visualizations in three figures, one for each studied year. These figures also feature regression lines to indicate the relationship between the Sad and Angry reactions. As we will see, this approach will allow us to gain some insights into the over-time development of this relationship. It will also allow for the identification and assessment of posts that had become especially popular in terms of Angry, Sad and Shares.

 

Visualization of the relationship between Sad, Angry and Shares per post during the 2017 elections
 
Figure 1: Visualization of the relationship between Sad, Angry and Shares per post during the 2017 elections.
Note: Larger version of Figure 1 available here.

 

While the orange trend line — relating to activity undertaken in relation to posts made by populists — provided a good fit for Angry and Sad (R2 = .65, p <.000), the fit for the blue trend line depicting the same relationship for non-populist actors was not quite as good (R2 = .20, p <.000). Indeed, the blue nodes appear as largely clustered to the left side of the figure, indicating limited amounts of Angry reactions in relation to these posts. The nodes representing posts by populist actors follow a different trajectory. As indicated by the aforementioned lines, we discern a stronger relationship between the two varieties of reactions mapped out in the figure above. The orange populist nodes are not only more spread out — they also appear to be larger than the blue nodes, indicating larger amounts of shares. Finally, the prevalence of square node shapes suggests the prowess of individual politicians over parties, again mirroring the results presented in Table 1. The posts identified in Figure 1 would seem to line up well with our expectations and indeed with the claim that “the architecture of Facebook is well-suited to activate anger among citizens” [17]. The identified posts arguably read like what we might expect from populist actors in terms of attacking other societal actors (e.g., Stanyer, et al., 2016) and focusing on immigration issues e.g., Klinger and Koc-Michalska, 2022). Moreover, the featured nodes — all by the right-wing populist Progress Party — also feature examples of actively encouraging users to engage by means of the Facebook affordances studied here. Indeed, such strategies have been previously employed by this party and their representatives (Larsson, 2023) — strategies that appear to work very well in order to reach higher amounts of shares.

 

Visualization of the relationship between Sad, Angry and Shares per post during the 2019 elections
 
Figure 2: Visualization of the relationship between Sad, Angry and Shares per post during the 2019 elections.
Note: Larger version of Figure 2 available here.

 

Much like for the 2017 data, the orange trend line — relating to populist activity — emerged with a better fit (R2 = .58, p <.000) than its blue counterpart (R2 = .28, p <.000). This latter, strengthened relationship between Sad and Angry for non-populists is also mirrored in the regression analyses presented earlier. Of note is also the 2019 tendency for posts made by non-populists to feature corresponding nodes of larger size — indicating more shares — than for the 2017 graph shown previously. The example non-populist posts tend to deal with large-scale societal or international issues, while the populist posts that are highly engaged with revolve around supposed restrictions on Christmas celebrations. This latter theme can indeed be seen as something rather typical for populist actors, invoking fears of erosion of “the cultural norms one knew since childhood” [18]. Moreover, while a few exceptions can be discerned, we again primarily see individual politicians leading in terms of gaining engagement as defined here.

 

Visualization of the relationship between Sad, Angry and Shares per post during the 2021 elections
 
Figure 3: Visualization of the relationship between Sad, Angry and Shares per post during the 2021 elections.
Note: Larger version of Figure 3 available here.

 

Much like for 2017 and 2019, the orange trend line for 2021 provides what could be considered an acceptable fit for the relationship between Angry and Sad (R2 = .43, p <.000), while the blue line emerges with a rather poor fit (R2 = .07, p <.000). These differences in fit can indeed be discerned in the figure. Specifically, compared to 2017 and 2019, the division between posts receiving plenty of Angry engagement and posts receiving plenty of Sad engagement emerges as well-defined, with posts largely bundled along the horizontal and vertical left axis respectively. While previous figures have shown posts placed in middle sections of both figures — suggesting posts enjoying comparatively larger amounts of the reactions under scrutiny — our final year of study indicates a distinctive clustering when comparing Angry and Sad. For the posts receiving higher amounts of Angry, we again see populists dominate — again using familiar emotionalizing techniques (Gil de Zúñiga, et al., 2020). For posts yielding comparatively higher amounts of Sad reactions, non-populists tend to discuss international issues — with one clear exception taking place during the electricity price increase that became prevalent in large parts of Europe during the winter of 2021.

 

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Discussion and conclusion

Engagement — such as the varieties studied here — was for a long time seen as a largely positive aspect of online activity (e.g., Carpentier, 2012). Summarizing the literature, Chadwick (2019) refers to such a viewpoint as being characterized by an “engagement gaze”, where researchers have supposedly been “conditioned [...] to look for evidence of engagement, and, wherever they find it, celebrate it as an unalloyed good”. Clearly reflective of the “utopian rhetoric that surrounds new media technologies” [19], the situation has more recently shifted towards a more pessimistic — perhaps even realistic — view on citizen participation in relation to political content (e.g., McChesney, 2013; Quandt, 2018). Relating specifically to the measure used as our dependent variable in the study presented here, John (2024, 2013) traced the rise and decline of ‘sharing’ as an important keyword for online activity — indeed, “no one (surely) thinks that social media are, in toto, a force for harmony, mutuality, and caring” [20]. Such changes are also discernable in the leaked internal Facebook documents and whistleblower testimonies (Reuning, et al., 2022). September 2018 saw the Facebook newsfeed team shift their priorities “from societal good to individual value” (Horwitz and Seetharaman, 2020), suggesting a more laissez-faire approach to hostile content. Similarly, 2019 saw Facebook employees realizing that recent algorithm changes had contributed to making the platform more harsh and aggressive in terms of the type of content that was being made visible (Hagey and Horwitz, 2021). Related to our case at hand, reports were made that Facebook employees heard from European political parties that they felt pressured to change policy positions and ways of phrasing such positions in order to increase engagement on Facebook (Zubrow, et al., 2021). Thus, the claim made by Jacobs and co-authors that “the architecture of Facebook is well-suited to activate anger among citizens” [21] appears to hold true.

The results presented here, then, suggests that such prioritization of negative emotions in relation to sharing behaviors has been in place in the Norwegian context since the ability to engage by means of Reactions was made available. Granted, we cannot know for sure how the inner workings of the algorithms utilized by Facebook control what gets seen and what does not. Nevertheless, our findings appear to be pretty much in line with what was to be expected given the developments outlined above and indeed in our hypotheses and research questions. Anger is indeed a potent force in both politics (Webster, 2020) and in social media engagement more generally (Eberl, et al., 2020; Larsson, 2018), and the finding that corresponding and similar Facebook reactions are related to the degrees to which that posts are shared will likely have repercussions for how campaigns are carried out. Studies in both the U.S. (Kreiss, et al., 2018) as well as in our current case country of Norway (Kalsnes, 2016) have shown that parties spend plenty of time and resources on analyzing voter behavior on platforms. Relatedly, results from Austria (Ennser-Jedenastik, et al., 2021) suggests that such insights also influence future party postings, leading them to focus more on the types of content that they know will generate reactions and, as shown here, shares (see also Kreiss and McGregor, 2017). As such, the results presented here further stresses the “warning sign” [22] proposed by Eberl and co-authors (2020). Specifically, as political actors are likely to construct their messages in order to reach as many potential voters as possible, the tendencies uncovered here as well as elsewhere might assist in creating an “incentive for parties to commit to negative campaigning” [23] — with possible repercussions of incivility (Hopp and Vargo, 2017) and further negativity and skepticism towards politics (Bene, et al., 2022).

All studies have limitations, and the present one is no exception. While the efforts presented here have provided insights into the relationships between different shares, comments, likes and reactions, the study design employed does not allow for investigations into the degree that reactions and shares, for instance, might mutually influence each other. A Facebook user might be more likely to share a post after they see a lot of angry reactions — presumably due to social validation processes or similar sociological phenomena. Different methods and approaches are needed to further investigate these relationships. Hopefully, the results presented here can provide inspiration for such endeavors. Moreover, while our efforts have provided useful insights into how Facebook engagement during election periods, further work might find it useful to more clearly leverage the time series nature of social media data. Date stamp variables are quite often available, and could, for instance, be used to investigate whether the increase of Angry reactions on a post at a specific time is connected to a rise in shares of posts at a specified later time. Much like as mentioned before, the results presented could hopefully be useful for such efforts.

In conclusion, then, the results presented here speak to the necessity of further longitudinal study. To be precise, as our current study has shown Anger and Sad reactions to be clearly related to the degree to which content gets shared, scholars might find it useful to detail the degree to which political actors are actually adapting to this tendency. Indeed, Bene and co-authors (2022) likens this process to the mediatization of politics (e.g., Strömbäck, 2008), discussing the “viralization” of politics (see also Bene, 2021). Jost (2023) considered similar developments, suggesting “accommodation” as a term for how “political actors use communication strategies that have previously met users’ expectations and stimulated their willingness to interact with messages” [24]. Regardless of which terminology one might prefer, researchers might find it useful to perform more in-depth analysis of how content provision at the hands of political actors changes over time. Are we seeing tendencies of the adaptation suggested previously? While the quantitative analysis presented above can optimistically provide a basis of sorts for such further exploration, the more qualitatively oriented examples pointed to could hopefully serve as a starting point of sorts for over-time content analyses — automated or not. End of article

 

About the author

Anders Olof Larsson, Ph.D., in the Department of Communication at Kristiania University College in Oslo, Norway.
E-mail: andersolof [dot] larsson [at] kristiania [dot] no

 

Notes

1. Eberl, et al., 2020, p. 48.

2. Bene, et al., 2022, p. 6.

3. Klinger and Koc-Michalska, 2022, p. 5.

4. Van Dijck and Poell, 2013, p. 7.

5. Bene, 2021, p. 338.

6. Bennett and Segerberg, 2012, p. 746.

7. Porten-Cheé, et al., 2018, p. 214.

8. Gerodimos and Justinussen, 2015, p. 117.

9. Pang, et al., 2016, p. 899.

10. Hayes, et al., 2016, p. 171.

11. Porten-Cheé, et al., 2018, p. 210.

12. Hayes, et al., 2016, p. 172.

13. Koiranen, et al., 2022, p. 197.

14. Eberl, et al., 2020, p. 60.

15. Brader and Cikanek, 2019, p. 203.

16. Heidenreich, et al., 2024, p. 7.

17. Jacobs, et al., 2020, p. 616.

18. Inglehart and Norris, 2017, p. 446.

19. Papacharissi, 2002, p. 9.

20. John, 2024, p. 1,903.

21. Jacobs, et al., 2020, p. 616.

22. Eberl, et al., 2020, p. 60.

23. Ibid.

24. Jost, 2023, p. 184.

 

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doi: https://doi.org/10.1080/19331681.2021.1881015, accessed 31 May 2024.

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Editorial history

Received 19 September 2023; revised 14 February 2024; accepted 31 May 2024.


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Angry sharing: Exploring the influence of Facebook reactions on political post sharing
by Anders Olof Larsson.
First Monday, Volume 29, Number 6 - 3 June 2024
https://firstmonday.org/ojs/index.php/fm/article/download/13215/11631
doi: https://dx.doi.org/10.5210/fm.v29i6.13215