Beyond progress vs. regress: Societal change as dynamic social evolution

Introduction

There are many areas where social psychologists might direct their energies profitably toward a study of power: a study of power conflict and resistance, the nature of victory and defeat, of acquiescence and accommodation; institutionalization, and the regulation of conflict, intensification of conflicts; and the pseudo-resolution of conflicts through deflection; through escape valves and through regulated emotional catharsis. Social psychologists might even ask even more fundamental questions such as “What do we mean by social change?” (Kenneth Clark, 1965, p. 18, “Problems of power and social change: Toward a relevant social psychology”)

Despite generational gains in human health and safety (Pinker, 2012; Shermer, 2015), many scholars view the world as in a state of dangerous regression. Modern rights and opportunities are seen as at risk of being reversed by fascist, authoritarian, ethno-nationalist, and other traditionalist political movements (see Graham & Svolik, 2020; Pettigrew, 2017). For instance, the political scientists Norris and Inglehart (2019) described the present historical moment as a populist “cultural backlash” against the liberal democratic advances of the modern period. Others describe this historical moment as one of political pushback, democratic decline, or democratic backsliding. Descriptions of pushback, backlash, decline, and backsliding in the present require a juxtaposition against their presumed opposite in the past. Thus, the pull backward in the present is presumed to be a regressive counter-movement to the past push forward along a liberal democratic arc bent toward justice (to paraphrase the Rev. Dr. Martin Luther King Jr.).

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As regressive as recent politics may appear, however, appearances can be deceiving. How societal change appears in any given period or place is affected by chosen time frames, imputed trajectories, and preferred directions (see Krus & Blackman, 1980; Vanneman & Cannon, 1987). Over-simplified glosses like progress and regress therefore flatten the complex, non-linear, and dynamic ways that people, and the systems in which they live, evolve over the time and events that make up history (Leach & Bou Zeineddine, 2021).

To best analyze how and why so many of our societies seem to be experiencing convulsive change, we must heed Kenneth Clark and ask what exactly is meant by change. One important first step is to acknowledge that “the only constant is change,” as Heraclitus put it in the fifth century BCE. Indeed, all living systems – whether atoms, people, or the universe itself – are in continual motion (Tyson et al., 2000). This is no less true of human societies (for reviews, see de la Sablonnière, 2017; Reicher & Haslam, 2012; Sztompka, 1993). Even the maintenance of apparent stability in society – any particular “status quo” – requires a great deal of psychological and social motion (for discussions, see Bou Zeineddine & Leach, 2021; Sidanius & Pratto, 1999; Vanneman & Cannon, 1987).

Given that change is constant, it is unwise to conceptualize societal change as progress or regress along a preferred, unidirectional, linear trajectory limited to an arbitrarily chosen time frame (Krus & Blackman, 1980; Leach, 2002; Perez et al., 2026; Vanneman & Cannon, 1987). Therefore, in this paper, we eschew the view of the political present as an epoch of backlash, pushback, backsliding, or decline (in contrast to a recent past of progress). Instead, we view societal change as the continual dynamic evolution of a system of (political, cultural, social, linguistic, economic, psychological) systems. Thus, we build on recent applications of systems thinking to the psychology of social issues to offer an overarching view of how to best conceptualize and study societal change (see Bou Zeineddine & Leach, 2021; Leach & Bou Zeineddine, 2021).

In the second section after this introduction, we explain some key limitations of presumptions of discrete epochs of linear societal change glossed as regress vs. progress. As initial illustrations we show that viewing authoritarianism, fascism, racism (and other isms) as dynamically evolving ideologies precludes any characterization of them as an “old-fashioned” animus that is a regression, backslide, or decline. To illustrate this process, we review theory and research on the presumed rise of political polarization into “echo chambers” – tight clusters of like-minded partisans that feed and fortify each other’s sentiment segregated from those who disagree (Barberá et al., 2015; Thurner et al., 2025). We show that the social evolution of polarization is dynamic rather than linear – it ebbs and flows in complex directions and evolves differently in different collectives. Thus, political polarization is not well characterized as a regressive political outcome of echo chambers. Political polarization is better examined using more dynamic thinking regarding the social evolution of human psycho-socio-political systems.

In the third section, we further the case for studying societal change as the dynamic social evolution of systems. We sketch an over-arching framework for understanding societal change, in part by discussing dynamic social evolution in terms of the relativity of time and the constancy of change.

In the fourth section, we briefly discuss appropriate methodological approaches to the dynamic study of societal change, including social media usage, computational simulation, and event-contingent time series. The paper concludes with an invitation to use the dynamic evolution of systems to examine a variety of changes encountered by psychologists interested in society.

Discrete Epochs of (progressive Vs. Regressive) Societal Change?

The many and varied fractures in the polity today are undeniable. For instance, mass protest for and against the state has risen to unprecedented levels in the twenty-first century (for a review, see Leach et al., 2024). This is partly why Norris and Inglehart (2019) described the present as an authoritarian populist “cultural backlash.” They argue that traditionalists are pushing back against the “silent revolution” of post-war democratic liberalism enabled by younger generations’ choice of transcendental values such as equality over material needs. Other political scientists worry about democratic decline, or backsliding as modern “progress” toward full enfranchisement is questioned or undermined (see Graham & Svolik, 2020). In essence, the fear is that traditionalist political movements’ ascent to power is based on reversals of what many liberal democrats took to be inviolable principles and policies (e.g., the welfare state, gender and ethnic equality, voting enfranchisement).

A Return of “old” Political Movements?

This “regression” is seen as enabled by the “rising” popularity and influence of “old-school” authoritarian leaders like Modi, Bolsonaro, Orbán, and Trump, as well as by religio-ethno-nationalist parties in Germany, Italy, Britain, France, Israel, India, Russia, and elsewhere (see Graham & Svolik, 2020; Kukharkin et al., 2026; Perez et al., 2026; Pettigrew, 2017). The appeal of authoritarian power and punishment, however, is not a regression or backslide to old politics (see also Perez et al., 2026). It is what it always is, in every period – strongman leadership that promises to put “deviants” in their place and otherwise offer succor to the traditionalists who seek security in subordination to a “righteous” and resolute leader (Adorno et al., 1950; Altemeyer, 1981; Pettigrew, 2016, 2017).

It is instructive to remember that Adorno and colleagues’ (1950) germinal study of authoritarianism focused on the “potential for fascism” in the United States in the immediate wake of the defeat of the Nazis. Their concern was that the (psychological, social, economic, political) insecurity of post-war change could render authoritarianism more appealing to everyday people seeking safety and security, even under the shadow of defeated and discredited Nazism. This dynamic view of (potential) societal change was enabled by Adorno et al.’s (1950) dynamic systems thinking, which was rooted in Freudian psycho-dynamics and Frankfurt School multi-level psycho-social theory. Thus, Adorno et al.’s (1950) original approach in The Authoritarian Personality viewed this psycho-social orientation as evolving over time and place in complex ways not easily rendered as a societal regression to a backwards fascism.

The present is not the past; it cannot be. These “old” ways of thinking are new; they have developed now in ways not possible in past politics. Thus, Le Pen is not Hitler. Modi is not Mussolini. The AfD (Alternative for Germany) party is not the Nazi party. As Griffin (2012) argued about fascism, it is at one and the same time a (re)new(ed) movement to progress the nation by reclaiming and repurposing the past. That is why Griffin (2012) uses the term palin-genetic to describe societal re-birth with

“[…] a coherent model of the ideological dynamics of situations when the cohesion of both traditional and modern societies break down and give rise to a new society arising Phoenix-like from the ashes of the old on the basis of a new world view or value-system.” (p. 12).

Qualifiers like “neo”-fascist themselves suggest the inadequacy of describing such movements as only a regression to some prior historical instance. If it is new fascism, then it is not simply old fascism.

Consider also Make America Great Again (MAGA). It is neither progressive nor regressive because it is both. It is forward-looking in its desire for future greatness, and it is backward-looking in its assertion that past greatness can be resurrected (Kukharkin et al., 2026; Lavie-Driver & van der Linden, 2026; Pettigrew, 2016, 2017). As such, MAGA is misunderstood if it is not seen as a movement of the present political moment marked by the distinctive contemporary realities of media para-sociality, globalization nausea, political polarization, gerrymandered voting, etc. Lavie-Driver and van der Linden (2026) offered a related analysis of President Putin. They argued that his vision for contemporary Russia as a new imperial power is based in a valorization of the old Soviet Union. They also describe the Kahanist movement in Israel as a repurposing of ancient beliefs and traditions to support the contemporary goal of a Jewish theocratic state.

Leach and colleagues made a similar point about the claimed resurgence of “old-fashioned” racism and xenophobia in Western European liberal democracies in the 1990s (e.g., Leach, 1998, 2002; Leach et al., 2000). The populist call to “send back” immigrants was old in some ways and new in some ways. Similarly, the “modern” racial resentment expressed by white Americans about increased racial equality in the 1980s and 1990s was new and old at the same time (Leach, 1998, 2002). That white racial resentment and calls for “repatriation” of immigrants have gained popularity in liberal democracies again in the 2020s illustrates that there is no simple linear progress or regress over time. “Old-fashioned” ideologies are not locked in a forever discredited past – they can become old and tired in one historical moment and can be made new and fresh in another historical moment.

Viewing societal change as dynamic social evolution enables better explanations of what otherwise appear as the bizarrely anachronistic “rise” of isms that seem both old and new at the same time (see Leach et al., 2000; Pettigrew, 2009, 2017). Viewing authoritarianism, fascism, racism (and other isms) as dynamically evolving precludes any characterization of them as a regression, backsliding, or backward push to “old-fashioned” animus. From another perspective, a longer view may frame current political convulsions as a return to form, as authoritarianism or fascism reassert themselves (e.g., Lavie-Driver & van der Linden, 2026; Perez et al., 2026). Or, a longer view may frame current distortions of democracy as yet another in the long line of incomplete instantiations of true freedom and equality (see Leach, 2002; Perez et al., 2026; Sidanius & Pratto, 1999). Viewing societal change as dynamic social evolution may also remind us that insecurity about the future need not drive people to authoritarianism or religio-ethno-nationalism, as Borghi et al. (2025) observed in Greek and British adolescents who instead embraced democracy, egalitarianism, and pluralism.

It is the imposition of arbitrary time frames and normative trajectories that creates the appearance of discrete epochs of pushback or backsliding in contrast to periods of apparent progress (Krus & Blackman, 1980; Vanneman & Cannon, 1987). Glosses – such as progress and regress, push forward and pushback – do not offer precise enough analyses of the form in which societal change evolves dynamically over the time and events that hashmark history.

Political Polarization Via Echo Chambers?

Political polarization is a very salient current example of a presumed regression to tribalism taken to represent democratic decline. The common refrain is that there has been a social sorting of politicians and people into opposing camps that cannot cooperate in ways necessary to democracy (for reviews, see Finkel et al., 2020; Iyengar et al., 2012; Smith et al., 2024). From legislative gridlock to voter disenfranchisement, competing conspiracies, and political violence, there are certainly deep antagonisms between political parties and between partisans today. In the U.S. House of Representatives, for example, Republicans and Democrats have rarely voted in the same way since the early 1980s (Andris et al., 2015).

In tight clusters commonly described as “echo chambers,” like-minded partisans are thought to feed and fortify each other’s sentiment (Barberá et al., 2015; Thurner et al., 2025). Within sycophantic circles some sort of centripetal force of mutual influence is often presumed to drive partisans toward those within their echo chamber (for reviews, see Finkel et al., 2020; Smith et al., 2024). Such homogeneous social networks – established by physical segregation or algorithmic manipulation (Bail et al., 2018; Rathje et al., 2021; Tucker et al., 2018) – are often represented as having maximal within-cluster connections and minimal between-cluster connections.

Although strong agreement within collectives can often move individuals’ sentiment toward the extreme, such within-group polarization is typically modest in effect (for reviews, see Brown, 1986; Smith et al., 2024; Turner, 1991). In addition, within-group polarization does not appear to be perpetual – it need not continue over time, let alone increase (see Barberá et al., 2015; Flache et al., 2017). Thus, the notion of echo chambers offers a less compelling explanation of political polarization than is commonly presumed (see also Smith et al., 2024). Part of the reason that echo chambers fall short is that the presumption of perpetual polarization within clusters seems too linear, simple, and static a process to account for the non-linear, complex, and dynamic ways that political polarization appears to operate.

Polarization Is Not Increasing Linearly

That political polarization is not on a simple linear trajectory is already suggested by the fact that voting in the U.S. House became markedly more polarized starting in the 1980s (Andris et al., 2015). Before then, there is little sign of polarization and little discernible change over time. And, there is little discernible increase in polarization after 1995, when the extreme disconnect between the parties appears to have been established.

It is also important to note that these “trends” would appear quite different if we presented party voting before 1949 and included prior periods of extreme polarization. The trends would also appear quite different if we only included party voting since 1995, after current levels of polarization became entrenched. Thus, the appearance of progress or regress over time is contingent on the time frame imposed by the given analysis (Krus & Blackman, 1980; Vanneman & Cannon, 1987). The characterization of change as progress or regress is also contingent on a presumption of what trajectory constitutes that sort of change. Again, as Kenneth Clark advised, one must specify what one means by change before one can assess it.

That political polarization is not a simple linear decline in liberal democracy is also suggested by the fact that polarization in people evolved later than in politicians. And, that polarization in people evolved in a more obviously non-linear pattern (Pew Research Center, 2014). In Pew Research Center (2014) data on the ideological divide between Republicans and Democrats from 1994 to 2015, Republicans and Democrats first moved left in parallel. Then, from 2004 to 2011, Republicans moved right ideologically in asymmetrical change. Only after 2011 did Republicans and Democrats move toward the more extreme poles of left and right ideology, and thus diverge dramatically from each other. Thus, in only one four-year period did there appear to be polarization in a 21-year time window considered to be the prime period of it.

Polarization Is Not So Simple or Inevitable

Many recent studies challenge the sufficiency of simple linear accounts of “increased” political polarization. Computational models, network science, and other contemporary approaches show that macro-level patterns of change, such as political polarization, emerge in complex ways over time (see Holme & Saramäki, 2012; Kozma & Barrat, 2008). Indeed, small differences in key parameters can push a population toward qualitatively different equilibria (Castellano et al., 2009). Even classical opinion dynamics models (e.g., bounded confidence models and voter models) show that simple models of social influence can lead to consensus, persistent disagreement, or bimodal polarization depending on a few key parameters (Flache et al., 2017; Hegselmann & Krause, 2002). For instance, Sasahara et al. (2021) showed that social influence alone is insufficient to explain the development of echo chambers in online discussion. This is partly because individuals selectively attend to, avoid, or disconnect from others (Duggins, 2017; Mäs & Flache, 2013; Sasahara et al., 2021; see also Thurner et al., 2025). In addition, individuals’ susceptibility to influence depends on their prior conviction and subsequent exposure (Chacoma & Zanette, 2015) as well as how strongly they stick to their views (Conjeaud et al., 2024). And, recent contagion and co-evolutionary opinion models of finance (Gai & Kapadia, 2010), emotion (Schweitzer et al., 2020), and more, show that external shocks can “rewire” and thus change the structure of networks (Castellano et al., 2009). Thus, there is nothing inevitable about echo chambers or other processes of within-group influence leading to extreme between-group polarization.

The presumed centripetal force of the influence within echo chambers appears to be an insufficient explanation of between-cluster polarization. At the very least, some sort of centrifugal force also seems to drive partisans away from those outside their “echo chamber” (see also Smith et al., 2024). Political polarization is, in fact, bi-polarization, as Flache et al. (2017) called it. Thus, to understand political polarization, we must examine how sentiment oscillates over time within and between collectives of individuals operating within a system of systems (see Leach & Bou Zeineddine, 2021). This requires viewing people as coalescing around a pole that defines them – and in which they invest psychologically and socially – in contrast to a seemingly opposed pole of equal value to its adherents (see also Smith et al., 2024).

The anthropologist Bateson (1935, 1958) offered an early systems-based approach to the social evolution of differences between collectives in language and culture that he called schismogenesis. For Bateson, interaction produces mutual, reciprocated action and reaction within and between collectives operating in one dynamic system (see also Leach & Bou Zeineddine, 2021). From this point of view, political polarization can be understood as a dynamic evolution of sentiment within a social system populated by individuals in complex relation (near and far, within and between collectives, inside and outside of latitudes of acceptance and rejection). It is through this dynamic social evolution that we can expect the otherwise “counter-intuitive” reality that more interaction between disagreeing collectives can feed, rather than starve, political polarization (e.g., Sasahara et al., 2021; Thurner et al., 2025). This dynamic explanation can be contrasted with the static assumption in the echo chamber explanation that insists that less interaction across segregated collectives necessarily leads to more political polarization.

Dynamical systems approaches to social evolution relevant to political polarization and the more general question of societal “progress” and “regress” also have a long history in social and personality psychology. Based in his field theory, Kurt Lewin examined psychological change within and between groups regarding democratic decision-making, collective sacrifice, and much more. A more dynamic approach to polarization within and between groups is also suggested in classic social psychological work in the social judgment (Sherif & Hovland, 1961) and social identity (Turner, 1991; Turner et al., 1987) traditions. Social judgment theory explained the dynamics of social influence by proposing that individuals are most influenced by those with sentiment similar enough to fall within the individual’s latitude of acceptance. Sentiment outside of this latitude of acceptance is easily ignored or dismissed and thus has little influence (i.e., non-commitment). Importantly, a very discrepant sentiment can fall within the individual’s latitude of rejection. Rejected sentiment can repulse individuals and thus influence them to move further away from it. This is referred to as a contrast effect (Sherif & Hovland, 1961; Turner et al., 1987).

The social identity approach to social influence within and between collectives built on these fundamental insights from social judgment theory (Turner, 1991; Turner et al., 1987). It conceptualizes the ways in which sentiment can be invested with psychological and social meaning once it is established as self-defining for collectives. Self-categorization theory views collective sentiment as a basis for social differentiation between opposing collectives who repulse one another enough to fuel their mutual movement away from one another and toward polar extremes.

Without the dramatic political polarization between opposed views fighting for supremacy, polarization toward one view would not be seen as so threatening to society (see also Finkel et al., 2020; Smith et al., 2024). Indeed, contemporary observers worry that poisonous politics has accelerated into a backwards tribalism of politicians and people in a winner-takes-all competition anathema to democracy. This goes well beyond the within-collective dynamic of an echo chamber that simply reinforces views by segregated exposure to similar others. To gloss this complex dynamic process as polarization is to flatten and simplify its dynamic relational contours operating both within and between collectives (i.e., bi-polarization).

What of Affective Polarization?

The concept of affective polarization describes the tendency for politically polarized groups to express mutual distrust and dislike (Finkel et al., 2020; Iyengar et al., 2012). Like political polarization, affective polarization has gained recent attention because it is presumed that political partisans have been growing increasingly negative toward each other (Garrett & Bankert, 2020; Iyengar et al., 2019; Lavie-Driver & van der Linden, 2026). For instance, Democrats and Republicans in the U.S. have grown increasingly cold toward each other over the last thirty years, moving from fairly neutral in 1990 to quite cold in 2024 (American National Election Studies, 2025). And, after the 2020 U.S. presidential election, individuals reported feeling “disgusted” or “fearful” toward members of the opposing party even in everyday interaction (Druckman et al., 2022; Pew Research Center, 2021). Recent studies, however, reveal that (moralized) affect and emotion can fuel non-linear “bursts” of political polarization. For example, some agent-based models use the dynamics of emotion to explain phenomena like radicalization (e.g., Schweitzer et al., 2020; Sobkowicz, 2012). Other models demonstrate how outrage can create tipping points for influence whereby agents become immune to persuasion once their anger surpasses a certain threshold. Such outrage can trigger reciprocated cascades in which agents become increasingly extreme in their views (e.g., Schweitzer et al., 2020).

In sum, it is increasingly clear that political polarization develops dynamically, rather than simply increasing linearly and evenly over time and space. Examining polarization in terms of democratic backsliding or regressive tribalism misses its most disturbing dynamism, such as feverish proselytization, committed extremism, recalcitrant rigidity, and deeply disaffected exit (Jost et al., 2018). Even the very notion of affective polarization seems to require a more dynamic approach to capture the way that (moralized) sentiment can super-charge political bi-polarization in the ways that may make it dangerous to democracy. Lavie-Driver and van der Linden (2026) use a case study to make a similar point regarding Trump supporters’ virulent hostility toward their political opponents providing a moralized rationale for the violent January 6, 2021, insurrection to keep Trump in the presidency by force.

Societal Change As Dynamic Social Evolution

Societal change tends to manifest in sudden bursts that are often triggered by reactions to emotionally charged events (e.g., Freelon et al., 2016; more generally, see Tufekci, 2017). These reactions fade as other events command attention or as interest and commitment diffuses or polarizes (for and against) over time. Thus, change is rarely linear (Barabási, 2005; Leach & Bou Zeineddine, 2021), whether for ape culture (Sapolsky, 2005), online financial markets (Kobayashi et al., 2018), or social movements (Leach & Teixeira, 2022; Leach et al., 2024).

Social Evolution in Relative Time

In many living systems – especially open systems like the social ecology of humans – time may be especially discontinuous (see Ferguson et al., 2026; Leach & Bou Zeineddine, 2021). In social network analysis, the discontinuous evolution of social connections is described as “bursty” (Barabási, 2005). In time series analyses of the non-linear evolution of phenomena (like stock prices), such temporal discontinuity is described as “spiky.” In brain activity indicative of cognitive and affective systems, temporal discontinuity is analyzed as an event-related potential waveform (e.g., Reinka & Leach, 2018). Similarly, Sapolsky (2005) documented numerous ways in which the culture of baboon troops evolved discontinuously – sometimes slow, sometimes fast – as these organisms responded to (sometimes cataclysmic) events.

When societal change is conceptualized as a difference between epochal states (of progress vs. regress, for example), researchers must choose at least two time points for comparison. Indeed, without reference to a previous time point, change cannot be gauged. To judge the motion of an object, one must compare at least two points within some specified frame of reference (Einstein, 1934). The difficulty is that judgments of motion are necessarily relative to the chosen frame of reference. For instance, to judge the change of position of a shooting star, its movement across the sky must be gauged with reference to some other celestial object, like a planet or another star. As there are many possible frames of reference, judgments of motion are always relative.

The same is true for the interpretation of societal change in human societies. To gauge the degree and form of societal change, one must compare a given temporal state to another state in the past or the future. However, these reference points are arbitrary. As such, the degree of societal change observed will depend on the reference point used for comparison. The prevailing view of societal change rarely acknowledges the relativity of this judgment and thus previous work often observes what it describes as absolute societal change when change can only be relative.

The problems with interpreting observations of societal change as absolute rather than relative are apparent in longitudinal analyses (for a detailed review, see Kukla, 1982). For example, Krus and Blackman (1980) performed a set of time series analyses on the large-scale societal change represented by war. They showed that the degree and pattern of change observed was dependent on the timescale (i.e., frame of reference) that they examined. When the wars between 1480 and 1945 were examined year to year, no discernible pattern of change was observed. From year to year, the number of wars did not appear to be meaningfully greater or lesser. However, examining 20-year intervals “showed” a cyclical pattern of war, whereby the number of wars increased on a regular basis and then declined until the next increase. Examination of 100-year intervals “showed” a different pattern, whereby the number of wars increased linearly. Thus, using 1-, 20-, and 100-year intervals as a frame of reference “showed” three different patterns of change. This illustrates the arbitrariness of choosing any one of these intervals as a frame of reference for assessing change over time. The arbitrariness of assessing change over time would be further shown if we compared the amount of change across only two time points (e.g., 1530 vs. 1920, or 1920 vs. 1930), as is commonly done in psychological research.

Whether focused on war, economic equality, or political power, the observation of societal change is always relative to the frame of reference used to establish a difference between a past and present time point. Like Krus and Blackman’s (1980) illustration with the frequency of war over time, empirical work on online financial systems shows that activity unfolds across multiple timescales. These examples underscore the general need to align analytic windows with the temporal structure of the phenomenon under study (Kobayashi et al., 2018). Those who make temporal observations without attention to their inherently relative nature are likely to misunderstand the degree and pattern of change in the system. To make the analysis of change less dependent on arbitrary choices of a frame of reference, it is essential to make intentional choices of timescale that are explained and evaluated (Holme & Saramäki, 2012).

Social Evolution in Constant Motion

Without an appropriate frame of reference, we may miss altogether the constant motion of society. Just as we fail to observe the movement of the stars in the sky without a reference point, we can fail to observe the continual movement of societal change. For instance, income inequality in the United States has been remarkably stable since 1967, increasing only slightly from a Gini coefficient of 0.40 in 1967 to 0.48 in 2011 (DeNavas-Walt et al., 2013). This historical trend can be taken as evidence that this degree of income inequality is a powerful form of social organization, resistant to change. As such, stable income inequality could be taken as evidence that inequality is inevitable and everlasting and that it is wrong to propose that disadvantaged groups can improve their status. Surely workers would have united by now to improve their position, if they had nothing to lose but their chains. However, societal change regarding income inequality may be very much like the night sky. It may appear to change very little when in fact it is changing continuously, in ways that we cannot easily observe as we change along with it. Other sciences, including physics, have recognized that apparent stability does not mean the absence of change:

“Everything moves. From the molecules of air around us to distant islands of stars, nothing sits still. We hear of comets crashing into planets, black holes gulping streams of gas, and space itself expanding like some vast balloon. And yet the night sky cloaks these cosmic motions. Apart from the wandering Moon and planets and an occasional meteor, the heavens don’t seem to change. Indeed, our eyes cannot see stars moving relative to each other from night to night, or even from one generation to the next.” (Tyson et al., 2000, p. 8)

Because societal change is continual, it is also important to note that features of social organization may remain stable partly because one form of societal change is neutralized by another form of change (Leach & Bou Zeineddine, 2021). For example, numerous U.S. policies and practices since 1967 have increased income inequality, such as inheritance laws, weakened unions, and the deregulation of private investment. Other policies and practices since 1967, such as unemployment insurance and wage growth, have decreased income inequality. The net effect of these countervailing examples of societal change has been stability in economic inequality over this particular 44-year period (Vanneman & Cannon, 1987). As in all systems, however, this stability was achieved through a great deal of societal activity and change, even if no aggregate change is easily observed in a particular metric.

Methodological Approaches to Studying Societal Change

Before concluding with an invitation to use the dynamic evolution of systems to examine a variety of changes encountered by psychologists interested in society, we discuss appropriate methodological approaches to the dynamic study of societal change, including social media usage, computational simulation, and event-contingent time series.

Social Media

The non-linear dynamics of time and motion in societal change appear to be intensified by social media (Bennett & Segerberg, 2012; Freelon et al., 2016; González-Bailón et al., 2013; Tufekci, 2017). This makes social media an especially important way to study societal change. This is one important reason for growing calls for psychologically grounded models of digital-era phenomena that go beyond structure to capture (dynamic) process (e.g., Bliuc et al., 2024; Levin et al., 2021; Marino et al., 2024).

Before it was rebranded as X in July 2023, Twitter served as a digital “public square” for easy and open discussion and information sharing unbound by physical constraint (Boyd, 2010; Bruns & Moe, 2014; Freelon et al., 2016; Wojcik & Hughes, 2019). As such, social media platforms like Twitter enable individuals to co-interpret and co-react to unfolding events within socially networked “discourse communities” in which a shared reality evolves over time (Freelon et al., 2016; Leach et al., 2024; Tufekci, 2017). Real communication networks, however, contain many under-observed influences such as algorithmic amplification, platform-specific affordances, historical conflicts, and uneven patterns of exposure (see also Lavie-Driver & van der Linden, 2026). These factors make it difficult to isolate specific behavioral mechanisms. One alternative is to generate a network with known structure and ground-truth parameters without noise from unmeasured social or algorithmic processes.

System Simulation

Computational simulation offers a relatively controlled and ethically unconstrained way to examine how micro-level “updates” accumulate into broad population patterns. In this sense, simulation functions as a “theoretical microscope” (Epstein, 1999; Smith & Conrey, 2007) that makes explicit how ordinary interpersonal updating can generate large-scale collective effects. Many widely used models of social influence use this generative logic. They illustrate how variation in micro-level update rules generates distinct macro-level outcomes. For example, in some opinion dynamics models, agents update their states through repeated interaction, adjusting their positions in response to others and producing clustering, consensus, or polarization over time (Castellano et al., 2009; Flache et al., 2017). In contrast, threshold models treat influence as a process of adoption or activation, where agents change state only when local exposure exceeds a critical threshold, generating cascade-like, system-wide shifts (Watts, 2002). Thus, simulation provides a repeatable framework for observing how dynamic societal outcomes form and why they persist under particular conditions (Epstein, 1999; Smith & Conrey, 2007; Watts, 2002).

Although social media and computer simulation offer two especially relevant methods to examine societal change as the dynamic evolution of systems, they are neither necessary nor sufficient. To address dynamic evolution in systems, any method can adopt a dynamic systems logic, or meta-theory, to some degree. Thus, traditional methods in social and behavioral science may be reoriented to systems meta-theory if they presume that their topic of study is a system that operates dynamically (for a discussion, see Leach & Bou Zeineddine, 2021). This is perhaps most easily illustrated with longitudinal or related methods that examine change over time.

Event-contingent Time Series

There is nothing inherent to longitudinal methods that presumes linear change over discrete epochs of continuous time. This way of thinking about change over time is simply presumed, rooted in a meta-theory that views change as simple, linear, and mechanistic (Leach & Bou Zeineddine, 2021). As people – and the events and systems within which they are embedded – interact dynamically, time is rendered especially discontinuous (e.g., Barabási, 2005; Reinka & Leach, 2018). As charismatic leaders, environmental catastrophes, misguided strategies, misunderstandings, faux pas, and other dramatic events incite action and reaction, temporal trajectories can change dramatically (e.g., Sapolsky, 2005). This can happen to stock prices after an economic report, baboon culture after a gender imbalance, or political polarization after a publicized murder of a controversial figure (e.g., Charlie Kirk).

Ferguson et al. (2026) took an event-contingent time series approach to examine the (non-linear) social evolution of Black Lives Matter and related discussion on Twitter over an especially momentous month in the summer of 2014 (Freelon et al., 2016). In that period, several unarmed Black people were killed by police (e.g., Eric Garner, Michael Brown), a national movement grew out of publicized protest in Ferguson, Missouri, Black Lives Matter became a more widely shared frame, and numerous politicians and public figures debated the issues.

With their event-contingent analysis of expressed belief and sentiment (see also Reinka & Leach, 2018), Ferguson et al. (2026) could show that discussion and sentiment changed dramatically from day to day as attention shifted from police killings, to protests, to police response to protest, to funerals and other public rituals. Thus, belief and sentiment changed trajectory often after dramatic events, rather than changing more steadily in a linear rise and fall over time. Black Lives Matter evolved as a frame for people’s experience quite late in the time period. And yet, it served as an especially potent predictor of subsequent positive sentiment in ways not paralleled by other discussion topics, such as “hands up, don’t shoot,” “Ferguson,” or “Mike Brown.” Thus, in late August 2014, Black Lives Matter emerged out of a collective online discussion as a shared way of sense-making. It was a complex dynamic produced by a system of systems through which collective belief and sentiment emerged organically out of the confluence of socially networked individuals reacting to an unfolding set of unpredicted events (see also Leach et al., 2024).

Conclusion

There are few topics as important to psychology – and allied social and behavioral sciences – as societal change. And, there are few topics as important to society as societal change. We have suggested that the imposition of arbitrary time frames and presumed trajectories creates the appearance of epochs of regress or progress when the reality of change is less discrete. Glosses such as progress and regress, push forward and pushback, are not precise enough descriptions of the exact form that societal change takes. Binary conceptions of change, such as progress and regress, are too linear, simple, and static to represent the ebb and flow within systems. It is therefore essential that scientist and citizen alike aim to understand societal change as it operates in the world – non-linear, complex, and dynamic across multiple systems (i.e., cognitive, affective, social, political, structural). To better understand the state of our societies we must better characterize their constant evolution in terms of the time and events that hashmark history.

Funding

This research received no external funding.

Author Contributions

CWL conceived of the idea and did most of the initial writing. STF wrote most of the review of the computational research and supported the other literature reviews. BBW supported the literature reviews and copyedited. All authors provided critical contributions to conceptualization, literature review, writing, and editing.

Competing Interests

The authors declare no competing interests.

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References

Adorno, T. W., Frenkel-Brunswik, E., Levinson, D. J., & Sanford, R. N. (1950). The authoritarian personality. Harper & Brothers.

Altemeyer, B. (1981). Right-wing authoritarianism. University of Manitoba Press.

American National Election Studies. (2025). ANES 2024 Time Series Study full release (August 8, 2025 version) [Data set and documentation]. https://www.electionstudies.org

Andris, C., Lee, D., Hamilton, M. J., Martino, M., Gunning, C. E., & Selden, J. A. (2015). The rise of partisanship and super-cooperators in the U.S. House of Representatives. PLOS ONE, 10(4), Article e0123507. https://doi.org/10.1371/journal.pone.0123507

Bail, C. A., Argyle, L. P., Brown, T. W., Bumpus, J. P., Chen, H., Hunzaker, M. B. F., Lee, J., Mann, M., Merhout, F., & Volfovsky, A. (2018). Exposure to opposing views on social media can increase political polarization. Proceedings of the National Academy of Sciences, 115(37), 9216–9221. https://doi.org/10.1073/pnas.1804840115

Barabási, A.-L. (2005). The origin of bursts and heavy tails in human dynamics. Nature, 435(7039), 207–211. https://doi.org/10.1038/nature03459

Barberá, P., Jost, J. T., Nagler, J., Tucker, J. A., & Bonneau, R. (2015). Tweeting from left to right: Is online political communication more than an echo chamber? Psychological Science, 26(10), 1531–1542. https://doi.org/10.1177/0956797615594620

Bateson, G. (1935). Culture contact and schismogenesis. Man, 35, 178–183. https://doi.org/10.2307/2789408

Bateson, G. (1958). Naven: A survey of the problems suggested by a composite picture of the culture of a New Guinea tribe drawn from three points of view (2nd ed.). Stanford University Press. https://doi.org/10.1515/9781503621138

Bennett, W. L., & Segerberg, A. (2012). The logic of connective action: Digital media and the personalization of contentious politics. Information, Communication & Society, 15(5), 739–768. https://doi.org/10.1080/1369118X.2012.670661

Bliuc, A.-M., Betts, J. M., Vergani, M., Bouguettaya, A., & Cristea, M. (2024). A theoretical framework for polarization as the gradual fragmentation of a divided society. Communications Psychology, 2(1), Article 75. https://doi.org/10.1038/s44271-024-00125-1

Borghi, O., Niraki, M., Seremeta, E., Smets, K., & Tsakiris, M. (2025). Facing a dark future: Young people’s future anxiety and political attitudes in the UK and Greece. advances.in/psychology, 2(1), Article e555124. https://doi.org/10.56296/aip00042

Bou Zeineddine, F., & Leach, C. W. (2021). Feeling and thought in collective action on social issues: Toward a systems perspective. Social and Personality Psychology Compass, 15(7), Article e12622. https://doi.org/10.1111/spc3.12622

Boyd, D. (2010). Social network sites as networked publics: Affordances, dynamics, and implications. In Z. Papacharissi (Ed.), A networked self: Identity, community, and culture on social network sites (pp. 47–66). Routledge. https://doi.org/10.4324/9780203876527-8

Brown, R. (1986). Social psychology (2nd ed.). Free Press.

Bruns, A., & Moe, H. (2014). Structural layers of communication on Twitter. In K. Weller, A. Bruns, J. Burgess, M. Mahrt, & C. Puschmann (Eds.), Twitter and society (pp. 15–28). Peter Lang.

Castellano, C., Fortunato, S., & Loreto, V. (2009). Statistical physics of social dynamics. Reviews of Modern Physics, 81(2), 591–646. https://doi.org/10.1103/RevModPhys.81.591

Chacoma, A., & Zanette, D. H. (2015). Opinion formation by social influence: From experiments to modeling. PLOS ONE, 10(10), Article e0140406. https://doi.org/10.1371/journal.pone.0140406

Clark, K. B. (1965). Problems of power and social change: Toward a relevant social psychology. Journal of Social Issues, 21(3), 4–20. https://doi.org/10.1111/j.1540-4560.1965.tb00502.x

Conjeaud, I., Lorenz-Spreen, P., & Kalogeratos, A. (2024). DeGroot-based opinion formation under a global steering mechanism. IEEE Transactions on Computational Social Systems, 11(3), 4040–4057. https://doi.org/10.1109/TCSS.2023.3330293

de la Sablonnière, R. (2017). Toward a psychology of social change: A typology of social change. Frontiers in Psychology, 8, Article 397. https://doi.org/10.3389/fpsyg.2017.00397

DeNavas-Walt, C., Proctor, B. D., & Smith, J. C. (2013). Income, poverty, and health insurance coverage in the United States: 2012 (Current Population Reports P60-245). U.S. Census Bureau. https://www.census.gov/library/publications/2013/demo/p60-245.html

Druckman, J. N., Klar, S., Krupnikov, Y., Levendusky, M., & Ryan, J. B. (2022). (Mis)estimating affective polarization. The Journal of Politics, 84(2), 1106–1117. https://doi.org/10.1086/715603

Duggins, P. (2017). A psychologically-motivated model of opinion change with applications to American politics. Journal of Artificial Societies and Social Simulation, 20(1), Article 13. https://doi.org/10.18564/jasss.3316

Einstein, A. (1934). Essays in science (A. Harris, Trans.). Philosophical Library.

Epstein, J. M. (1999). Agent-based computational models and generative social science. Complexity, 4(5), 41–60. https://doi.org/10.1002/(SICI)1099-0526(199905/06)4:5<41::AID-CPLX9>3.0.CO;2-F

Ferguson, S. T., Leach, C. W., Wondimu, B., Anand, N., & Cogburn, C. (2026). Before the backlash: The early evolution of Black Lives Matter on Twitter [Manuscript in preparation]. Barnard College, Columbia University.

Finkel, E. J., Bail, C. A., Cikara, M., Ditto, P. H., Iyengar, S., Klar, S., Mason, L., McGrath, M. C., Nyhan, B., Rand, D. G., Skitka, L. J., Tucker, J. A., Van Bavel, J. J., Wang, C. S., & Druckman, J. N. (2020). Political sectarianism in America. Science, 370(6516), 533–536. https://doi.org/10.1126/science.abe1715

Flache, A., Mäs, M., Feliciani, T., Chattoe-Brown, E., Deffuant, G., Huet, S., & Lorenz, J. (2017). Models of social influence: Towards the next frontiers. Journal of Artificial Societies and Social Simulation, 20(4), Article 2. https://doi.org/10.18564/jasss.3521

Freelon, D., McIlwain, C. D., & Clark, M. D. (2016). Beyond the hashtags: #Ferguson, #BlackLivesMatter, and the online struggle for offline justice. Center for Media & Social Impact, American University. https://doi.org/10.2139/ssrn.2747066

Gai, P., & Kapadia, S. (2010). Contagion in financial networks. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, 466(2120), 2401–2423. https://doi.org/10.1098/rspa.2009.0410

Garrett, K. N., & Bankert, A. (2020). The moral roots of partisan division: How moral conviction heightens affective polarization. British Journal of Political Science, 50(2), 621–640. https://doi.org/10.1017/S000712341700059X

González-Bailón, S., Borge-Holthoefer, J., & Moreno, Y. (2013). Broadcasters and hidden influentials in online protest diffusion. American Behavioral Scientist, 57(7), 943–965. https://doi.org/10.1177/0002764213479371

Graham, M. H., & Svolik, M. W. (2020). Democracy in America? Partisanship, polarization, and the robustness of support for democracy in the United States. American Political Science Review, 114(2), 392–409. https://doi.org/10.1017/S0003055420000052

Griffin, R. (2012). The legitimizing role of palingenetic myth in ideocracies. Totalitarismus und Demokratie, 9(1), 39–56. https://doi.org/10.13109/tode.2012.9.1.39

Hegselmann, R., & Krause, U. (2002). Opinion dynamics and bounded confidence: Models, analysis and simulation. Journal of Artificial Societies and Social Simulation, 5(3), Article 2. https://www.jasss.org/5/3/2.html

Holme, P., & Saramäki, J. (2012). Temporal networks. Physics Reports, 519(3), 97–125. https://doi.org/10.1016/j.physrep.2012.03.001

Iyengar, S., Lelkes, Y., Levendusky, M., Malhotra, N., & Westwood, S. J. (2019). The origins and consequences of affective polarization in the United States. Annual Review of Political Science, 22(1), 129–146. https://doi.org/10.1146/annurev-polisci-051117-073034

Iyengar, S., Sood, G., & Lelkes, Y. (2012). Affect, not ideology: A social identity perspective on polarization. Public Opinion Quarterly, 76(3), 405–431. https://doi.org/10.1093/poq/nfs038

Jost, J. T., van der Linden, S., Panagopoulos, C., & Hardin, C. D. (2018). Ideological asymmetries in conformity, desire for shared reality, and the spread of misinformation. Current Opinion in Psychology, 23, 77–83. https://doi.org/10.1016/j.copsyc.2018.01.003

Kobayashi, T., Sapienza, A., & Ferrara, E. (2018). Extracting the multi-timescale activity patterns of online financial markets. Scientific Reports, 8(1), Article 11184. https://doi.org/10.1038/s41598-018-29537-w

Kozma, B., & Barrat, A. (2008). Consensus formation on adaptive networks. Physical Review E, 77(1), Article 016102. https://doi.org/10.1103/PhysRevE.77.016102

Krus, D. J., & Blackman, H. S. (1980). Time-scale factor as related to theories of societal change. Psychological Reports, 46(1), 95–102. https://doi.org/10.2466/pr0.1980.46.1.95

Kukharkin, A., Barber, F., Cooley, E., Caluori, N., Brown, X., Singh, A., Cipolli, W., & Brown-Iannuzzi, J. L. (2026). White Americans’ feelings of being “last place” are associated with anti-DEI attitudes, Trump support, and Trump vote during the 2024 U.S. presidential election. advances.in/psychology, 1(1), Article e549398. https://doi.org/10.56296/aip00046

Kukla, A. (1982). Logical incoherence of value-free science. Journal of Personality and Social Psychology, 43(5), 1014–1017. https://doi.org/10.1037/0022-3514.43.5.1014

Lavie-Driver, N., & van der Linden, S. (2026). Weaponising the past: An extended SIMCA model for how social identity and collective memory shape variation in collective action responses to democratic backsliding. advances.in/psychology, 1(1), Article e719332. https://doi.org/10.56296/aip00055

Leach, C. W. (1998). Toward a social psychology of racism? Comments on “On the parallels between social cognition and the ‘new racism’” by N. Hopkins, S. Reicher & M. Levine. British Journal of Social Psychology, 37(2), 255–258. https://doi.org/10.1111/j.2044-8309.1998.tb01170.x

Leach, C. W. (2002). Democracy’s dilemma: Explaining racial inequality in egalitarian societies. Sociological Forum, 17(4), 681–696. https://doi.org/10.1023/A:1021033608845

Leach, C. W., & Bou Zeineddine, F. (2021). A systems view of emotion in socio-political context. Affective Science, 2(4), 353–362. https://doi.org/10.1007/s42761-021-00051-z

Leach, C. W., Ferguson, S. T., & Teixeira, C. P. (2024). Protest now: A systems view of 21st century movements. Group Processes & Intergroup Relations, 27(5), 1188–1206. https://doi.org/10.1177/13684302241245660

Leach, C. W., Peng, T. R., & Volckens, J. S. (2000). Is racism dead? Comparing (expressive) means and (structural equation) models. British Journal of Social Psychology, 39(3), 449–465. https://doi.org/10.1348/014466600164507

Leach, C. W., & Teixeira, C. P. (2022). Understanding sentiment toward “Black Lives Matter.” Social Issues and Policy Review, 16(1), 3–32. https://doi.org/10.1111/sipr.12084

Levin, S. A., Milner, H. V., & Perrings, C. (2021). The dynamics of political polarization. Proceedings of the National Academy of Sciences, 118(50), Article e2116950118. https://doi.org/10.1073/pnas.2116950118

Marino, E. B., Benitez-Baleato, J. M., & Ribeiro, A. S. (2024). The polarization loop: How emotions drive propagation of disinformation in online media—The case of conspiracy theories and extreme right movements in Southern Europe. Social Sciences, 13(11), Article 603. https://doi.org/10.3390/socsci13110603

Mäs, M., & Flache, A. (2013). Differentiation without distancing. Explaining bi-polarization of opinions without negative influence. PLoS ONE, 8(11), Article e74516. https://doi.org/10.1371/journal.pone.0074516

Norris, P., & Inglehart, R. (2019). Cultural backlash: Trump, Brexit, and authoritarian populism. Cambridge University Press. https://doi.org/10.1017/9781108595841

Perez, M. J., Beam, A. J., & Small, P. A. (2026). Race, memory, and colorblindness: Critical history and deconstructing United States democracy. advances.in/psychology, 1(1), Article e316437. https://doi.org/10.56296/aip00053

Pettigrew, T. F. (2009). Post-racism? Putting President Obama’s victory in perspective. Du Bois Review: Social Science Research on Race, 6(2), 279–292. https://doi.org/10.1017/S1742058X0999018X

Pettigrew, T. F. (2016). In pursuit of three theories: Authoritarianism, relative deprivation, and intergroup contact. Annual Review of Psychology, 67(1), 1–21. https://doi.org/10.1146/annurev-psych-122414-033327

Pettigrew, T. F. (2017). Social psychological perspectives on Trump supporters. Journal of Social and Political Psychology, 5(1), 107–116. https://doi.org/10.5964/jspp.v5i1.750

Pew Research Center. (2014, June 12). Political polarization in the American public. https://www.pewresearch.org/politics/2014/06/12/political-polarization-in-the-american-public/

Pew Research Center. (2021, November 9). Beyond red vs. blue: The political typology. https://www.pewresearch.org/wp-content/uploads/sites/20/2021/11/PP_2021.11.09_political-typology_REPORT.pdf

Pinker, S. (2012). The better angels of our nature: Why violence has declined. Penguin Books.

Rathje, S., Van Bavel, J. J., & van der Linden, S. (2021). Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences, 118(26), Article e2024292118. https://doi.org/10.1073/pnas.2024292118

Reicher, S. D., & Haslam, S. A. (2012). Change we can believe in: The role of social identity, cognitive alternatives, and leadership in group mobilization and social transformation. In B. Wagoner, E. Jensen, & J. A. Oldmeadow (Eds.), Culture and social change: Transforming society through the power of ideas (pp. 53–74). Information Age Publishing. https://doi.org/10.1108/978-1-61735-759-620251006

Reinka, M. A., & Leach, C. W. (2018). Racialized images: Tracing appraisals of police force and protest. Journal of Personality and Social Psychology, 115(5), 763–787. https://doi.org/10.1037/pspa0000131

Sapolsky, R. M. (2005). The influence of social hierarchy on primate health. Science, 308(5722), 648–652. https://doi.org/10.1126/science.1106477

Sasahara, K., Chen, W., Peng, H., Ciampaglia, G. L., Flammini, A., & Menczer, F. (2021). Social influence and unfollowing accelerate the emergence of echo chambers. Journal of Computational Social Science, 4(1), 381–402. https://doi.org/10.1007/s42001-020-00084-7

Schweitzer, F., Krivachy, T., & Garcia, D. (2020). An agent-based model of opinion polarization driven by emotions. Complexity, 2020, Article 5282035. https://doi.org/10.1155/2020/5282035

Sherif, M., & Hovland, C. I. (1961). Social judgment: Assimilation and contrast effects in communication and attitude change. Yale University Press.

Shermer, M. (2015). The moral arc: How science and reason lead humanity toward truth, justice, and freedom. Henry Holt and Company.

Sidanius, J., & Pratto, F. (1999). Social dominance: An intergroup theory of social hierarchy and oppression. Cambridge University Press. https://doi.org/10.1017/CBO9781139175043

Smith, E. R., & Conrey, F. R. (2007). Agent-based modeling: A new approach for theory building in social psychology. Personality and Social Psychology Review, 11(1), 87–104. https://doi.org/10.1177/1088868306294789

Smith, L. G. E., Thomas, E. F., Bliuc, A.-M., & McGarty, C. (2024). Polarization is the psychological foundation of collective engagement. Communications Psychology, 2(1), Article 41. https://doi.org/10.1038/s44271-024-00089-2

Sobkowicz, P. (2012). Discrete model of opinion changes using knowledge and emotions as control variables. PLoS ONE, 7(9), Article e44489. https://doi.org/10.1371/journal.pone.0044489

Sztompka, P. (1993). The sociology of social change. Blackwell.

Thurner, S., Hofer, M., & Korbel, J. (2025). Why more social interactions lead to more polarization in societies. Proceedings of the National Academy of Sciences, 122(44), Article e2517530122. https://doi.org/10.1073/pnas.2517530122

Tucker, J. A., Guess, A. M., Barberá, P., Vaccari, C., Siegel, A. A., Sanovich, S., Stukal, D., & Nyhan, B. (2018). Social media, political polarization, and political disinformation: A review of the scientific literature. William and Flora Hewlett Foundation. https://doi.org/10.2139/ssrn.3144139

Tufekci, Z. (2017). Twitter and tear gas: The power and fragility of networked protest. Yale University Press.

Turner, J. C. (1991). Social influence. Open University Press.

Turner, J. C., Hogg, M. A., Oakes, P. J., Reicher, S. D., & Wetherell, M. S. (1987). Rediscovering the social group: A self-categorization theory. Blackwell.

Tyson, N. D., Liu, C., & Irion, R. (2000). One universe: At home in the cosmos. Joseph Henry Press. https://doi.org/10.17226/9585

Vanneman, R., & Cannon, L. W. (1987). The American perception of class. Temple University Press.

Watts, D. J. (2002). A simple model of global cascades on random networks. Proceedings of the National Academy of Sciences, 99(9), 5766–5771. https://doi.org/10.1073/pnas.082090499

Wojcik, S., & Hughes, A. (2019, April 24). Sizing up Twitter users. Pew Research Center. https://www.pewresearch.org/internet/2019/04/24/sizing-up-twitter-users/