6.2 Bipolarization and the new media

(see also Philosophy sister project: Information sharing and social identity)


The main aim is to investigate how and why the new media (email, the internet, social media; online communication more generally) may have increased socio-political conflict and bipolarization reducing the chances of sustainable cooperation in discourse and decision-making using or informed by these new media. This question is partly inspired by (but not restricted to) the rise of political populism in which politicians and others have used such media to advance divisive ideas and agenda’s (e.g. Trump on twitter), as well as the increasing reliance on social media and online outlets for news. The proposal outlines a series of experiments to investigate the social psychological processes that can explain distinctive effects of these media.

Background (and state of the art)

The new media (email, internet, social media) have so fundamentally changed the way people communicate that the label ‘gamechanger’ does not seem misplaced. But has this technological change influenced the degree of social cooperation and the sustainability thereof? The properties of these new media, and thus their effect are complex. While eschewing technological determinism, there may be reasons to be pessimistic. One example that comes to mind is the rise of (often rightwing) populism in which political leaders (like Trump) sidestep traditional institutions including media (press, TV) and use social media (like twitter) to communicate directly with the masses. A quick search of the research literature (van Hees, pers. comm.) confirms that the confluence of populism and social media is a hot topic (11k+ hits). The question then is not whether the new media are invoked in political trends like populism, but how and why certain trends and processes and outcomes manifest themselves, in our case with specific consequences for sustainable cooperation. In short, populism is just one topical (popular!) example. There is always likely to be diversity and disagreement in political discourse (not necessarily a bad thing) but the more general question is how do these new media impact on and interact with political discourse in ways that might exacerbate, or attenuate, political conflict? If we understand this we might also understand better ways to reduce conflict and increase sustainable cooperation. A first step is to appreciate and understand the properties of the new media, and how these contrast with other established forms. A number of key features and their possible consequences can be identified.

  • Anonymity and isolation. Anonymity and isolation are distinct features but they may often co-occur and have related effects so we consider them together here. Compared to face-to-face or offline communication, online communication often involves the (visual) anonymity and (physical) isolation of interlocutors. Some early researchers of CMC (e.g. Kiesler) argued that that anonymity could have deleterious social consequences by reducing social cues and accountability leading to social deregulation and anti-normative behaviour. Phenomena such as “flaming” (aggressive communication/posts) and more extreme group behavior were explained in this light. If true this could undermine social cooperation. However, social identity researchers (including us) have argued that social identity and group norms can paradoxically become more salient and impactful in the relatively anonymous and depersonalized context of online communication providing scope for greater social influence based on group identity (see Spears, 2021). A key question then is whether group identity based influence might foster or hinder bipolarization based around contrasting group identities. Social isolation has similarly led to contrasting conclusions about social cohesion/cooperation depending on whether the model of social cohesion is based on interdependence and reciprocity (in which case isolation would undermines this) or is based on shared group identity (in which case isolation would not affect and might even enhance it: see Spears; 2021).
  • Text-based/digital communication. Online communication is often (but not always) text based. This can make communication less effective and less nuanced and many researchers have pointed to this as a potential source of conflict, irritation, resulting in more direct and less nuance communication. This could reduce the chance of cooperation. The text-based nature can however increase the possible that one’s communication is traceable (e.g. to an IP address, documented threads, twitter feeds etc) increasing accountability but also concerns of surveillance (Spears, 1994; Spears & Postmes, 2015).
  • Reach/range. The ability to communicate with others across physical borders and develop a “following” (e.g., on twitter, streaming and other social media), increase the potential power of one’s social influence. Mass influence was once the purview of the mass media, but now individuals have the potential to influence many as never before.
  • Selectivity/targeting/diversity. Before the new media, socio-political discourse was influenced locally (by family, friends and local “offline” networks) and by the mass media. Hence the ‘water cooler’ moments where people would discuss TV output in the knowledge that other would have seen the same programs on a limited number of the channels the night before. Now people are increasingly getting their news on-line, with much more choice and variety and less limited by few (often right wing/corporate) political sources in the mass media. Rather than being exposed to political discourse “top down” from the old print and electronic media, people can seek their own information but this may lead to confirmation of their own views and values. The ability to transmit one’s own views to many (3 above) has also increased the number and diversity of information sources (Youtubers, streamers, influencers, twitterati). As a result information is increasingly fragmented, with more chance to remain in homogeneous political bubbles of like-minded others. Conversely this can democratizing and increase the diversity of sociopolitical discourse. What are the implication for conflict vs consensus and cooperation? This may increase cohesion within particular camps or wings of the political spectrum but reduce exposure to countervailing opinion. The ability to select information and target messages could thus help to explain negative as well as positive effects in the internet era.

These themes/questions speak to all three sustainability threats highlighted in SCOOP but particularly the theme of vicious cycles (self-reinforcing and patterns of behavior). If people are now more able to choose their sources of political information, rather than being passively exposed to these through traditional print and electronic media, then there is increasing chance that they will choose like-minded sources (“preaching to the converted”), in their political bubbles where fake news and conspiracy theories can take root. Under such conditions people are likely to seek and value information from the in-group (Postmes et al., 2001) creating the self-reinforcing feedback cycles that undermine openness to different views and perspectives. Anonymity and isolation may accentuate these effects (Spears & Postmes, 2015).

Description of the project/design and methods.

The aim of this project is to examine the psychological processes occurring in these new communication media, and how their technological features affect information sharing and social influence. Given resource constraints and theoretical priorities we focus on 1 and 4 from our analysis above (e.g. isolation, anonymity, and increased choice of information sources, and ability to target information socially) also because we think these features are most relevant to questions of conflict vs. cooperation in socio-political discourse. Theme 1 is especially relevant to the SIDE model (Spears & Lea, 1994;Spears & Postmes, 2015; Spears 2021).

We will develop a paradigm where we manipulate the features described above using communication software for verbal or text-based communication (e.g. Google meet, whatsapp groups, etc) that can communicate, over several sessions, as the specific design requires. In a typical study a group of students or other participants (e.g. using community samples from Prolific or Mturk) would be introduced to each other and arrangements made for the discussion of a particular topic/dilemma, over a number of sessions. Within this paradigm we can then manipulate the (visual) anonymity of participants, their isolation etc. For example communication could occur within the same lab online or isolated and online (Spears, et al, 2002). The program software allows for webcam vs. no camera, and also for verbal vs. text-based chat. Pretesting would allow us to measure people’s attitudes on discussion topics before hand (pretest/time=0) and this also enables the manipulation of the homogeneity vs. heterogeneity of political opinion. Discussion topics would focus on specific policy proposals similar to the classic choice dilemmas used in classical group polarization research (Spears et al., 1990). In certain studies (Expts 5,6) Ps would be given access to online mass media, or given more freedom to search the internet for sites to inform their opinions (see theme 4 above). Specific predictions would be informed by the considerations outlined above (Background; see 1 & 4) and more specifically our previous research comparing computer-mediated communication (CMC) with face-to-face (FtF) communication (e.g., Spears & Lea, 1994; Spears, 20121; Spears & Lea, 2015). We now outline specific studies and predictions.

Expt 1 & 2: This will use a 2(Anonymity: Anonymous vs. visible) x 2(Political views: Heterogeneous vs. Homogeneous) x session (time: 1-4) between Ps design with repeated measures on the time/session factor. The policy discussion will be on topics with an ideological right vs. left dimension with Ps giving their initial individual attitudes/choices in a pretest session (t=0; this also allows us to manipulate the homogeneity/heterogeneity factor). At t=4 Ps will give their own individual views (as in t=0), unconstrained by the need to reach a group consensus (as in t1-3) Predictions: Based on the SIDE model (Spears & Lea, 1994; Spears & Postmes, 2015), we predict that there will be greater and quicker convergence on a decision consensus (i.e. cooperation) when there is more prior homogeneity, and under conditions of anonymity (greater depersonalization and convergence on a group norm) (i.e. 2 and 3-way interactions), whereas heterogeneity is likely to lead to subgroup development and bipolarization (convergence on opposing camps), also accentuated over time. However under heterogeneous group conditions, visibility should help to prevent bipolarization (and possibly foster consensus) by individuating participants and detracting from subgroup formation. Experiment 1 with students would be followed up with a conceptual replication using a community sample (Expt 2).

Expt 3 & 4: Expt 3 will use a 2(Isolation: co-present vs. Isolated) x 2(Political views: Heterogenous vs. Homogeneous) x session (time: 1-4) between Ps design with repeated measures on the time/session factor, similar to above except for the isolation factor. In Experiment 3 all participants would be visible (enhancing identifiability and accountability to other group members). Predictions: We predict quicker convergence on consensus and thus cooperation when Ps are co-present (strongest accountability: a “strategic” SIDE model prediction) for both homogenous and heterogeneous groups, but that this effect would likely rebound at t=4, especially for heterogeneous groups, when they are no longer visible and thus accountable to the group. Isolation would give Ps freedom to follow their preferred subgroup options. Experiment 4 would be a conceptual replication of Expt 3 but now with Ps visually anonymous rather than visible. The lack of identifiability/accountability should weaken and the speed of consensus/cooperation effect in the co-present condition, and may foster subgroup formation.  

Expt 5 & 6. Expt. 5 (student sample) will use a 2(Information source choice: Imposed/narrow vs. Free/broad) x 2(Political views: Heterogeneous vs. Homogeneous) x session (time: 1-4) between Ps designs with repeated measures on the time/session factor, similar to above except for the Information choice factor. Ps would be allowed to search for additional information to inform their opinions from links to website news feeds that manipulate the range of political views design to reflect traditional mass media (e.g. NOS, Volkskrant, NRC, Telegraaf) vs. a more diverse range of purely online media excluding traditional news media (including twitter feeds and more extreme options from left and right). Predictions: We predict quickest convergence on a consensus decision for more homogeneous groups with a more narrow mass media information source and slowest convergence on consensus for heterogeneous groups with a free and broad choice of information sources. Replication in a community sample would have similar predictions

Russell Spears (Psychology)

Hedy Greijdanus (Psychology)

Jan-Willem Romeijn (Philosophy)

Leah Henderson (Philosophy)


Kiesler, S., Siegel, J., & McGuire, T. (1984). Social psychological aspects of computer-mediated communications. American Psychologist, 39, 1123-1134.

Postmes, T., Spears, R., Sakhel, K., & De Groot, D. (2001). Social influence in computer-mediated groups: The effects of anonymity on social behavior. Personality and Social Psychology Bulletin, 27, 1243-1254.

Spears, R. (2021). Social influence and group identity. Annual Review of Psychology. 72, 367-390.

Spears, R., & Lea, M. (1994). Panacea or panopticon? The hidden power in com­puter-mediated communication. Communication Research, 21, 427-459.

Spears, R., & Postmes, T. (2015). Group identity, social influence and collective action online: Extensions and applications of the side model. In S. Shyam Sundar (Ed.) The Handbook of Psychology of Communication, (pp. 23-46) Oxford: Wiley-Blackwell.

Spears, R., Lea, M., Corneliussen, R.A., Postmes, T., & Ter Haar, W.  (2002). Computer-mediated communication as a channel for social resistance: The strategic side of SIDE. Small Group Research, 33, 555-574.

Spears, R., & Tausch, N. (2015). Prejudice and intergroup relations. In M. Hewstone, W. Stroebe & K. Jonas (Eds.): Introduction to Social Psychology 6th Edition. (pp. 439-487), Oxford: Wiley-Blackwell.

Location: University of Groningen

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