Protests and Legislative Reform: An Empirical Approach to Tilly’s “WUNC” Framework

Author: Ioana Podarita

August 21, 2025

Abstract

Protests have the potential to influence cultural, social, and political environments. Charles Tilly argued that protests which display high worthiness, unity, numbers, and commitment (WUNC) are more successful at achieving their goals and having longevity. A limited number of studies have examined Tilly’s WUNC empirically, and no known study has analyzed WUNC in relation to legislative reform. The current pilot study aimed to bridge this empirical gap, by developing a preliminary WUNC measurement and analyzing legislative action in the United States following the seven biggest protests and movements in the past decade. Results indicate no significant correlation between WUNC and legislation reform, however, a distinct Pearson’s correlation between the digital presence of a protest and legislative reform was significant. As the number of anti-government demonstrations grows in the U.S., this paper discusses past findings on protests and legislation, current obstacles in protest research, and future directions. 

Introduction

American citizens, for centuries, have demanded fundamental rights through collective action in the form of protests [41, p. 1-12]. From the suffragette, civil rights, and gay liberation movements, to today’s anti-ICE and anti-government demonstrations [18], American citizens have employed this democratic tool to demand policy change. The exact definition of a protest varies among scholars [16], with Tarrow (1991) [38] defining it as “disruptive collective action” against elites and institutions, and Carey (2006) [11] focusing on “confrontational activity” against government policies. 

Charles Tilly, influential scholar of protests and social movements, described them as “a sustained challenge to power holders in the name of a population living under the jurisdiction of those power holders by means of repeated public displays of that population’s worthiness, unity, numbers, and commitment,” i.e., “WUNC” [42]. Tilly [41, p. 1-8] argued that analyses of collective action inherently focus on “power and politics,” and stem from instances of injustice. The study of protests therefore observes the people who act against those in power, and what they accomplish through their actions [41, p. 1-8]. Recent acts of protest and their outcomes are shaped by the cultural, technological, and political contexts they exist in [9, 33, 31]. 

[Image source: Brink News]

A Decade of Protests

Protests have surged in recent years, triggered by increasing social, economic, and political inequality [31, pp. 1-3]. Research [31, p. 3] suggests a recent pattern of more “political” protests, where demonstrators are motivated by dissatisfaction with politicians and governments and protest against them, rather than for a specific issue. In the last decade, the strongest reason to protest for citizens across partisan lines was protecting “real democracy” [31, p. 3]. There were 16,000 anti-government protests between 2017-2020 alone, including the five largest in the country’s history [6, p. 1]. These coincided with the political shift marked by President Trump’s first term, and ranged in topics from climate change, women’s and LGBTQ+ rights, and racial injustice [6, p. 10]. Mass protests fueled by perceptions of government inefficiency and corruption are expected to continue, as the United States and the world continue to battle political instability, weak economies, and civil unrest [6, p. 1]. 

The protests from this decade have been able to mobilize masses at such a historic rate with the help of digital activism [26; 6, p. 15]. The Internet allows movements to connect with a wider audience, lower the costs of organizing, mobilize beyond a centralized location, and makes it easier for citizens to self-mobilize [26]. The internet is an accessible tool that grassroots organizers can use in order to reach global audiences and maintain momentum even beyond a limited time of in-person demonstrations, thus evolving into movements and increasing chances of impact [27]. MeToo, one of the largest movements of the decade, had a very small in-person turnout, but it was able to affect real cultural, political, and legal change through its monumental digital reach [24]. Other protests such as the Women’s March, March for Our Lives, and, most recently, Black Lives Matter also either started on or expanded through the internet. By empowering otherwise powerless grassroots voices, digital protesting has become a key tool to increase political awareness, mobilize beyond country borders, and house long-lasting movements [27, 26, 33]. 

Protests’ Goals and Effectiveness

In person or online, protests generally have two possible outcomes - to influence public opinion and behavior and to result in policy changes and legislation [36]. While the cultural impact of protests is backed by a large body of literature, whether or not protests influence policy change remains understudied [9; 32, p.82]. Several factors make studying the political success of protests difficult [22]. First, these large movements often encompass diverse groups and organizations, who protest differently and likely have different views of success [22]. Even if a protest results in a legislative change, not all members of the movement may view it as a successful change [22]. Second, it is difficult to ascertain whether a policy change is directly linked to a protest, or if it would have happened anyways [22]. The issue of causality makes it difficult for scholars who study protests’ impact to claim a certain protest directly led to a policy change [22]. 

Costain & Majstorovic (1994) [14]  analyzed the number of bills related to women’s issues passed by Congress, and New York Times coverage of women’s issues, during the women’s movement (1950-1986). They found a moderate correlation between the social movement and legislation passed, with public opinion acting as an “intervening variable.” They suggest using correlational models of analysis over causal models,  since their findings suggest that social movements can shift public opinion, which in turn may pressure Congress to act in favor of the movement. Similarly, Burstein (1979) [8] found that media coverage of the civil rights movement increased public awareness of civil rights. With the general public becoming more educated on and engaged in civil rights issues, Congress finally passed the Civil Rights Act of 1964. Thus, social movements might not directly influence legislation, but they can create the cultural environment necessary for legislation to be enacted [8]. 

Legislators may be more influenced by public opinion than the social movements themselves because they rely on the public in order to get reelected [9]. Politicians are more likely to respond to a movement’s demands when those demands reflect the majority opinion [9]. Including outside factors, such as political parties, election years, and interest groups in analyses of protests’ success can offer a more accurate view of the protests’ effectiveness [22]. 

Anatomy of a Successful Protest - WUNC

Even within a favorable context (e.g. cooperative politicians, favorable media coverage, sympathetic audience), a protest needs to display four specific components in order to have strength (to achieve its goals): worthiness, unity, numbers, and commitment, or “WUNC” [42]. Tilly believed that social movements and protests differ from other forms of contentious politics (such as wars or electoral campaigns) by challenging authorities and publicly displaying WUNC. Tilly mentioned WUNC in more or less abstract forms throughout his entire career, but in How Social Movements Matter [42] he provides a more detailed breakdown of each element.

Worthiness refers to the protesters themselves and their behavior, and he gives the specific examples of “sobriety, propriety of dress,” and “endorsement of moral authorities” [42]. Unity can be displayed by protesters through marching, dancing, chanting, and cheering together, wearing similar symbols and uniforms, and affirming a common message [42]. Numbers can refer to either the actual number of protestors, or it can also be signaled through petitions, associations, polls, and financial contributions [42]. Finally, members can show their commitment by participating in risky or costly activities, resisting attacks, and committing to persevere [42]. 

Tilly (1999) [42] claims that protests have relied on variations of WUNC to achieve success for centuries, and they continue to use WUNC in current demonstrations. He also stresses the weight of each individual factor on the overall strength of the protests, concluding that if one factor drops to zero, the strength falls to zero and the protest loses credibility [42]. However, he notes, higher values in one factor (such as worthiness or unity) can make up for lower values in another (numbers) [42]. As Castaneda (2025) [12] reinforces, Tilly was not so much interested in the individual mentalities of protesters, but rather the public performance of WUNC. He believed protests reflected the popular sentiment of a state at a given time in history, and recording and documenting these events was part of the process of democratization [12]. 

Protest Data and Research

Social scientists rely on event databases in order to document the origins, characteristics, and outcomes of protests [19]. These event “catalogs” monitor the people who protest, the size and frequency of protests, reasons for protest, and the geography of demonstrations [17]. Historical data on protests can also show trajectories of movements and how they interact with social and political processes [19]. The earliest event databases recorded strike activity in the 19th century, but the practice of cataloguing demonstrations became popularized in the 1960s [19]. The need for protest data became apparent as interest in contentious politics research grew, most notably through Tilly’s work on social movements and democratization [19]. Tilly himself used newspapers to create catalogs of contentious events in Britain [41, 5]. Increased efforts to procure event data started in the 1980s, with the Dynamics of Collective Action (DoCA) Project, which hand-coded data on US protests from the New York Times reports [19]. 

Researchers have since adopted advanced techniques to document large sets of event data, such as computerized natural language processors, but they still rely on third-party information for these datasets [19]. Prominent contemporary projects, such as the Kansas Event Data System (KEDS) and the Global Database of Events, Language, and Tone (GDELT), use global news coverage of events to compile their databases [19]. As of right now, no similar projects exist that focus specifically on protest data in the United States after 1995 [19]. Since comprehensive event datasets like KEDS or GDELT are not exclusive to protests, they lack several key details that are necessary for protest research [19]. 

Contemporary studies of protests run into several issues due to reliance on news coverage data [19]. First, crowd size estimates often vary drastically between news outlets, and without an official police count, different sources provide different and vague estimates, or don’t provide them at all [19]. Second, studies tend to gather data from a small number of sources, which might omit details relevant to protestors’ reasons for participation, increasing the risk of bias [19]. Lastly, only a small number of protests are large enough to generate substantial media coverage, and smaller protest events are either unreported or very limited information is covered [5].

For a study trying to analyze Tilly’s WUNC in the context of real protest events, finding detailed news coverage of protests is one of the biggest obstacles. For such a study to be reliable, news sources must include congruent reports of protests’ numbers, protesters’ behavior and message, and obstacles overcome to participate. 

Past Research on WUNC and Present Study

Operationalizing WUNC is an additional challenge. Tilly is credited with pioneering empirical sociological methods [12], but he never provided an actual measurement of WUNC during his lifetime. A few studies have explored WUNC empirically, however. Bailey et al. (2023) [2] developed a psychometric measure of WUNC and found that WUNC impressions correlate with bystanders’ perceptions of a protest and intention to mobilize. Geise et al. (2023) [21] conducted a qualitative study of media portrayals and WUNC perceptions. Similarly, Freelon et al. (2016) [20]  analyzed WUNC components in social media posts and how they influenced news coverage of the Black Lives Matter movement. Wouters & Walgrave (2017) [45], though not empirical nor peer-reviewed, offered a helpful review of the history of WUNC in Tilly’s writings and the related literature of social movements. 

The present study aims to analyze the impact of WUNC displays on legislative outcomes following the seven biggest protests and movements in the last decade. The limited empirical literature on WUNC suggests that WUNC perceptions can influence one goal of movements - public perception and mobilization. This small, pilot study seeks to observe the relationship between WUNC and the second goal of movements - policy change. 

H1: WUNC displays will positively correlate with legislation changes following mass protests in the last decade. 

Method

Protest Selection

The protests (N = 7) included in the analysis were selected if they had over 100,000 participants, occurred between 2010-2020 in the United States, and specifically advocated for policy and legislative change. If a protest had recurring demonstrations, only the initial protest was included (i.e. the earliest one). The protests selected were: 1) The George Floyd Protests, 2) The 2017 Women’s March, 3) March For Our Lives, 4) March for Science, 5) People’s Climate March, 6) September 2019 Climate Strikes, 7) MeToo. The MeToo movement was included despite having a small in-person turnout because its digital presence far surpassed 100,000 participants. The WUNC scale (Cronbach’s alpha = .61) was developed based on Tilly’s (1999) [42] scorecard. The data used to assign values to protest variables was compiled from multiple news sources and polls of the protests, and hand coded for analysis. 

Coding the Variables

Legislation was coded on a 1-5 scale (1 - not at all; 2 - minimal, city and state wide changes; 3 - moderate, 1-4 states adopted changes; 4 - substantial, more than 5 states adopted changes; 5 - federal changes), based on legislative changes directly related to the demands of the protest, enacted the year of the protest and the following year. The protests ranged in scores from 2-5, with a mean score of 4 (SD = 1). 

Worthiness was broken down into 3 components: 1) whether participants were violent (1 - yes, 2 - no); 2) whether riots occurred (1 - yes, 2 - no); and 3) whether property damage occurred (1 - yes, 2 - no). Items “riots” and “property damage” were dropped to increase reliability. Higher values reflected higher Worthiness, with a highest total possible score of 2, and a mean score of 2 (SD = 0). 

Unity was broken down into 1) issue (1 - protesters advocated for different, non-related issues; 2 - single issue or cluster of related issues shared by all); 2) visual presentation (1 point for similar clothing, 1 point for similar signs and chants, 1 point for marching together and staying physically close); and 3) whether within-group conflict occurred (1 - yes, 2 - no). Item “conflict” was removed to increase reliability. Higher values reflected higher Unity, with a total possible score of 5, and a mean score of 4.14 (SD = 0.37). 

Numbers were broken down into in-person attendance and digital presence (based on the number of tweets during the protest). The values for each category ranged from one to seven, as follows: 1) 1 - under 100,000 participants; 2) 2 - 100,000 - 400,000; 3) 500,000 - 800,000; 4) 900,000 - 1.2 million; 5) 1.3 million - 3 million; 6) 4 - 6 million; 7) over 7 million. The highest possible score was 14 (seven for each), with a mean score of 8.42 (SD = 3.86). 

Commitment was broken down into 1) Duration (1 - single day; 2 - 2-7 days; 3 - 14 days; 4 - longer than 14 days; 5 - longer than 1 month; 6 - over a year); 2) whether travel was required to participate (1 - yes, 2 - no); 3) whether there were safety risks, such as disease or bodily harm (1 - yes, 2 - no); 4) whether there was increased risk of arrest (1 - yes, 2 - no); and 5) whether the protest turned into a movement (1 - yes, 2 - no). Variables 2-4 were reverse coded. Item “travel” was removed to increase reliability. Higher values reflected higher Commitment, with a highest possible score of 12, and a mean score of 6 (SD = 2.38). 

Worthiness, Unity, Numbers, and Commitment totals were summed in order to obtain a total WUNC score. The highest possible WUNC score was 33, with a mean score of 20.57 (SD = 5.59). A visual distribution of the Legislation and WUNC scores can be seen in Graph 1 and the means and standard deviations in Table 1 (Appendix). 

Several control variables were also analyzed. Table 2 (Appendix) shows data about the majority race, gender, and age of the protestors. “Funding” shows how many of the protests received external funding. “EntityOrgs” shows how many of the protests were organized by an established entity, and “PartnerOrgs” shows how many of them partnered with other organizations. Finally, “Centralized” shows how many protests were centralized to a singular location versus multiple locations. 

Table 3 (Appendix) shows control variables related to the context of the protest. Variables “PartyHouse,” “PartySenate,” and “PartyOffice” refer to the majority political party in the House, Senate, and Office, respectively. “Elections” shows whether or not the protest happened ahead of major Congress elections. “Trigger event” refers to whether the protests were triggered by an event that caused bodily harm or death. “ForceByState” shows whether the state used excessive force on the protesters. Lastly, “Anti-Government” refers to whether or not the protests were anti-government in nature. 

WUNC

Results

A Pearson’s correlation test revealed a weak positive correlation (R =0.06, p = .89) between Legislation and WUNC scores. The correlation matrix can be visualized in Graph 2. 

An additional test, with the item “Numbers” divided into the number of people present at the in-person protest (CrowdCount) and the number of Tweets of the protest (DigitalCrowd) revealed a strong positive correlation (R = 0.9, p < .05) between Legislation and Digital Crowd. The results can be seen in Graph 3. This test did not include two variables, March for Science and the September Climate Strikes, as data could not be found on the number of Tweets of the protests.

The protests were made up of predominantly white (57%) and female (86%) participants, over the age of thirty (43%). All of the protests in the sample received external funding. All but one (the George Floyd protests) were organized by an established entity, and all of them partnered with other organizations. Only one protest was centralized to one location (MeToo). 

Almost all of the protests occurred while there was a majority of Republicans in the House (71%) and Senate (86%), and only one protest happened while a Democrat was in Office. Four of the protests (57%) occurred ahead of major Congress elections, and three of them were triggered by an event involving death or bodily harm. The state used force during only one protest (the George Floyd protests), and three out of the seven protests had distinct anti-government messages. 

Discussion

WUNC and Legislation

The original hypothesis was not supported, with WUNC displays of the biggest recent protests not correlating with legislation implemented. One possible reason is that protests, regardless of WUNC displays, may not influence policy-makers unless they represent a large majority sentiment [9]. The MeToo movement had a score of five on legislation, meaning that it coincided with federal legislative changes, despite an average WUNC score (20) and very low in-person turnout. The People’s Climate March had the lowest legislation score (2), but was only a few points below MeToo on WUNC (16). The argument of public opinion as an intervening variable [14] could help explain the differences between these two protests. At the time of the MeToo protest, a majority of Americans identified sexual harassment as a major issue and a priority [35]. Conversely, in 2014, when the People’s Climate March happened, most Americans did not view climate change as a serious enough threat to take action [30]. If protests can’t influence policy change without supportive public opinion, as Burstein (1999) [9] suggests, it could help explain why WUNC displays did not correlate with legislative reform in the present sample. 

An opposing argument that could explain the insignificant results is that protests can influence policy-change, but different styles of protests might lead to different results. Shuman et al. (2024) [36] found that non-disruptive peaceful protests (normative) are more successful at mobilizing sympathetic audiences; however, disruptive and even violent protests (non-normative) are more effective at influencing policy change. The normative-nonnormative variable most closely relates to Tilly’s (1999) [42] “worthiness,” the variable in which all of the protests in the sample received the highest possible score. A non-normative protest would entail a lower “worthiness” score, and possibly a lower total WUNC score, but might correlate stronger with legislation, as per Shuman et al.’s (2024) [36] findings. A sample with an increased “worthiness” standard deviation could provide more insight into how WUNC displays correlate with legislation, even negatively. 

Lastly, a third possible reason concerns the selection criteria by size. The protests selected were the largest in the last decade, with over 100,000 in-person protestors (except MeToo), and the average score for legislation was four out of five. In a more diverse sample, the specific WUNC component “numbers” might correlate significantly with increased legislative action. A large body of literature supports the claim that the size of a protest influences policy-making [10]. McAdam and Su (2002) [29] suggest “disruption” and “signalling” models best explain how large protests might lead to policy changes [10]. Large protests pressure governments to concede to their demands by disrupting the country’s economy, as they can affect GDP levels, damage international credibility, and temporarily disrupt businesses [10, 25]. Larger protests are also more costly to contain and monitor, leading to increased security spending by the government [15]. 

The size of the protests signals to policy makers how much the demands represent popular opinion, how the popular opinion will influence future elections for the incumbent, and how likely the government is to contain the protesters [10]. A popular signalling model by Lohmann (1993) [28] suggests that size, combined with the identity and motivations of the protestors, can signal to the government whether or not concessions are required. For instance, the sudden mobilization of moderate individuals signals that the government might need to concede to contain dissent [10]. An appropriate example in the present sample is the March for Science (2017), which coincided with federal legislative changes, earning the highest possible score for legislation. The march was organized before president Trump announced his proposed budget for that year, but thousands of people mobilized after he proposed funding cuts to scientific institutions and research [34]. The sudden and substantial mobilization of scientists, who have been historically nonpolitical actors, might have led Congress to dismiss the proposed budget cuts, and even increase funding for science only a few weeks later [40]. 

Participants in the March for Science pass by the U.S. Environmental Protection Agency in Washington, on April 2017. [Image credit: Sait Serkan Gurbuz / AP Photo]

WUNC displays did not correlate significantly with legislative reform in the present sample, but it might prove beneficial to pursue relationships between WUNC, public opinion, and policy making in more diverse samples in order to accurately observe WUNC effects. 

Digital Protests

The strong correlation between legislation and digital presence of the protests was not surprising, considering recent literature analyzing the impact of social media communication on movements’ impact [26]. As technology changed the way we communicate, protests have increasingly relied on online communication to mobilize and reach large audiences [26, 23]. Grassroots organizations and individuals use social media to synchronize large assemblies in multiple locations, offer supporters different means of participation, and attract broader audiences [33]. 

The easy style of online communication can also help movements protect themselves from counteracts. Lee & Chan (2016) [26] found that digital media proved especially useful in disproving rumors by opponents meant to delegitimize the movement. They also found that individuals who engaged in digital protesting were more likely to have deeper involvement in the movement, by spending more time protesting in-person, mobilizing others to participate, and providing support on the frontlines [26]. 

Social media can also help improve the visual image of a protest, Hatuka (2023) [23] suggests. The Women’s March on Washington coordinated their action repertoire, slogans, and simultaneous timing primarily online [23]. They crafted the “sea of pink” (Zweiman, 2019) wearing hats by the “Pussy hat Project,” and coordinated the unified image online [23]. Aside from the visual message, the organizers of the Women’s March shared important resources to protesters such as a map of the route, an outline of protester’s rights, and instructions about respecting local and federal law on their website [23]. In the case of the Women’s March, social media aided the meticulous organizing and public image of a large-scale, decentralized protest. 

Unorganized protests can also benefit from social media communication. Bennet and Segerberg’s (2012) [3] model of connective action describes a type of protest that does not rely on attentive organization in order to attract large audiences. In this model, individual protestors could connect on social media at high enough rates to generate a large-scale movement, specifically because they are not bound by rigid forms of action [26]. Such was the case of the George Floyd protest, the only one in the sample not organized by an established entity. The protests erupted a day after officer Derek Chauvin murdered George Floyd, and a video of Chauvin kneeling on Floyd’s neck went viral [39]. 

The anti police brutality protests first sparked in Minneapolis, where Floyd was arrested and killed, but quickly spread to the rest of the country and the world [39]. The following days, online users began posting black squares on social media, in what became known as #BlackOutTuesday [13]. While not the organizers of the protests, the Black Lives Matter hashtag, which had been circulating online since 2015, peaked after George Floyd’s murder, with 24.5 million tweets in the summer of 2020 [4]. The viral digital reach of the movement translated into an in-person turnout of at least fifteen million people around the country, with some estimates going as high as twenty six million people, making it the largest movement in US history [7]. 

Beyond mobilization, protestors used social media to pressure representatives to work on comprehensive police reform [44]. The George Floyd Justice in Policing Act was passed by the House in 2021, but never passed the Senate. The bill, born out of millions of people demanding an end to police brutality [44], would have limited the use of force by law enforcement and restricted excessive measures like chokeholds and carotid holds. The main scope of the bill was to address racial profiling by law enforcement, and while it was not federally implemented, at least thirty states enacted laws directly related to policing reform [37]. 

Together with sizable protests, social media may have the potential to empower social issues enough to lead to legislative change [44]. The findings of the present study suggest a similar pattern, however, they are part of too small a sample to be anything more than suggestive. Two protests, the September Climate Strikes and March for Science, were excluded from the correlation as data regarding their digital presence could not be identified. These findings thus serve as a stepping stone for future research. As in-person and digital protests continue to work in tandem [26, 33], future studies of social movements and policy making would be wise to analyze digital impact as well. 

Limitations and Future Research

Several limitations affect the reliability of this study’s results. First, the study relied on third-party data, as most studies on protests, which can vary greatly among sources or are limited. Reports of digital protests were even more difficult to identify than in-person estimates, making the significant correlation between digital presence and legislation limited in scope. Second, due to news coverage and protest polls being the most detailed for larger protests, the present study only analyzed a very small sample of protests. These larger protests had more readily available information about the different WUNC components, but the results generated cannot be applied to different types of protests without further analysis. Third, the WUNC scale had questionable reliability, with a Cronbach’s alpha of .61. As this study represents one of the earliest known attempts at operationalizing WUNC based on Tilly’s theory, it is likely it omitted factors that could have improved scale reliability. Lastly, the study did not measure public opinion, which seems to be one of the most powerful predictors of policy change in the United States. 

Even with these limitations, the results of the present study add to a limited but growing body of literature on WUNC and protest events. Future studies could continue research on WUNC, legislation, and public opinion by observing a larger and more diverse sample of protests, accounting for intervening variables, and building upon existing WUNC measurements to develop an all-encompassing, reliable scale. Most importantly, the study echoes previous concerns related to protest data being highly reliant on news coverage and only being reported for larger protests. Polls conducted by social scientists at protests can offer more insight into the who, where, and why people protest, but they are far fewer than the multitude of news sources offering limited information. In order for research studies to accurately observe the makeup and impact of protests, including digital protests, datasets using first-party data need to be developed. 

Conclusion

Protests, though not directly related to legislative reform, can increase social awareness, promote the formation of new political platforms, and shift public opinion [43, 14]. As public opinion changes, and pressures decision-makers in Congress and in Office, state and federal legislative action becomes more likely [9]. Some protests are more successful than others, depending on the outcome they seek and the type of mobilization they display [36]. The size of protests might be especially important, with larger protests being more successful at disrupting the status quo and signaling the protests’ strength and message [10]. The identity of protestors can also influence legislators, as sudden mobilizations of moderate citizens signal a need for governments to concede to demands [10]. Digital activism is proving to be a key ally for protestors to activate wider audiences [26]. Social media can empower grassroots voices, protect movements from outside attacks and misinformation, and help organizers craft a unified image and message [26, 23, 33]. Online communication may also help mobilize large, unorganized crowds and pressure legislators to address protest demands [26, 44]. 

As the numbers, identities, behaviors, and messages of protestors’ seem to influence the outcomes of large movements, Tilly’s (1999) [42] framework of worthiness, unity, numbers, and commitment merits further analyses. Continuing to document these four variables in contemporary protests holds not only historical value, but is necessary as the United States and the world experience political unrest and instability [1]. 

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Glossary

• Action repertoire – set of various protest-related tools and actions available to a movement or related organization in a given time frame.

• Ascertain – find (something) out for certain; make sure of.

• Bystander – a person who is present at an event or incident but does not take part.

• Causality – In research, causality refers to the relationship between an event (the cause) and a second event (the effect), where the cause is partly responsible for the occurrence of the effect.

• Centralized – (of an activity or organization) controlled by a single authority or managed in one place.

• Comprehensive – including or dealing with all or nearly all elements or aspects of something.

• Concede – surrender or yield.

• Contentious Politics – the use of disruptive and often public actions by groups to challenge or influence political decisions or power structures.

• Control variables – factors that researchers hold constant or limit during a study to prevent them from influencing the relationship between the independent and dependent variables.

• Cronbach’s alpha – a measure of internal consistency, often used to assess the reliability of a set of scale or test items.

• Dissent – a lack of agreement or opposition to a particular viewpoint or course of action.

• Empirical – based on, concerned with, or verifiable by observation or experience rather than theory or pure logic.

• Enact – to make something into a law or to put something into effect, especially by legal or official means.

• Entail – involve (something) as a necessary or inevitable part or consequence.

• Fundamental rights – a set of basic human rights that are considered essential for a dignified life.

• GDP – Gross Domestic Product, a monetary measure of the total market value of all the final goods and services produced within a country's borders in a specific time period, typically a year.

• Generate – produce or create.

• Grassroots – a grassroots movement is one that uses the people in a given district, region or community as the basis for a political or social movement.

• Hypothesis – a proposed explanation for a phenomenon, often stated as a testable prediction based on prior knowledge or observations.

• Incumbent – the holder of an office or post.

• Intervening variable – a concept used in research to explain the relationship between an independent and dependent variable.

• Jurisdiction – the official power to make legal decisions and judgments.

• Meticulous – showing great attention to detail; very careful and precise.

• Mobilization – the act of assembling and preparing resources for a specific purpose.

• Momentum – strength or force gained by motion or by a series of events.

• Natural language processors – a field of Artificial Intelligence that focuses on enabling computers to understand, interpret, and generate human language, both written and spoken.

• Operational – how a concept or variable will be measured, observed, or manipulated within a study.

• Pearson’s correlation – a statistical measure that assesses the linear relationship between two continuous variables.

• Peer-reviewed – a process by which something proposed (as for research or publication) is evaluated by a group of experts in the appropriate field.

• Pioneer – develop or be the first to use or apply (a new method, area of knowledge, or activity).

• Pilot Study – a small-scale trial run of a larger research project, designed to identify potential problems, refine research methods, and assess feasibility before committing to the full-scale study.

• Police brutality – excessive and unwarranted use of force by law enforcement against an individual or a group.

• Preliminary – preceding or done in preparation for something fuller or more important.

• Procure – obtain (something).

• Prominent – important; famous.

• Psychometric measure – a standardized and quantifiable way to assess psychological attributes like personality, cognitive abilities, and aptitudes, typically using tests and questionnaires.

• Qualitative study – a research approach that focuses on gaining in-depth understanding of experiences, opinions, and behaviors through non-numerical data.

• Racial profiling – the use of race or ethnicity as grounds for suspecting someone of having committed an offence.

• Rigid – not able to be changed or adapted.

• Scope – the extent of the area or subject matter that something deals with.

• SD – standard deviation, a measure of how spread out a set of values is, relative to its mean (average).

• Sentiment – a view or opinion that is held or expressed.

• Status quo – the existing state of affairs, especially regarding social or political issues.

• Surge – a sudden large increase.

• Tandem – two people or things that work together to achieve a result.

• Third-party information – obtained from a source other than the primary parties involved.

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Sources

1. ACLED. US crisis monitor. (2025). ACLED. https://acleddata.com/us-crisis-monitor/#reports. 

2. Bailey, E. R., Wang, D., Soule, S. A., & Rao, H. (2023). How Tilly’s WUNC works: Bystander evaluations of social movement signals lead to mobilization. American Journal of Sociology, 128(4), 1206–1262.

3. 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. 

4. Bestvater, S., Gelles-Watnick, R., Odabaş, M., Anderson, M., & Smith, A. (2023). Ten years of #BlackLivesMatter on Twitter. Pew Research Center. https://www.pewresearch.org/internet/2023/06/29/ten-years-of-blacklivesmatter-on-twitter/. 

5. Biggs, M. (2018). Size matters: Quantifying protest by counting participants. Sociological Methods & Research, 47(3), 351–383.

6. Brannen, S., Haig, C., & Schmidt, K. (2020). The age of mass protests: Understanding an 

escalating global trend. Center for Strategic and International Studies. https://apo.org.au/node/277646.  

7. Buchanan, L., Bui, Q., & Patel, J. K. (2020). Black lives matter may be the largest movement in U.S. history. The New York Times. https://www.nytimes.com/interactive/2020/07/03/us/george-floyd-protests-crowd-size.html.

8. Burstein, P. (1979). Public opinion, demonstrations, and the passage of antidiscrimination 

legislation. Public Opinion Quarterly, 43(2), 157–172.

9. Burstein, P. (1999). Social movements and public policy. In How social movements matter (Vol. 10, pp. 3–21).

10. Butcher, C., & Pinckney, J. (2022). Friday on my mind: Re-assessing the impact of protest size on government concessions. Journal of Conflict Resolution, 66(7–8), 1320–1355.

11. Carey, S. (2006). The dynamic relationship between protest and repression. Political Research Quarterly, 59(1), 1–11.

12. Castañeda, E. (2025). Charles Tilly’s theories about the origins of European nation-states, social movements, contentious politics, and democracy. American Behavioral Scientist, 69(7), 921–943.

13. Chang, H. CH., Richardson, A., & Ferrara, E. (2022). #JusticeforGeorgeFloyd: How Instagram facilitated the 2020 Black Lives Matter protests. PLoS ONE, 17(12), e0277864. https://doi.org/10.1371/journal.pone.0277864.

14. Costain, A. N., & Majstorovic, S. (1994). Congress, social movements and public opinion: Multiple origins of women's rights legislation. Political Research Quarterly, 47(1), 111–135.

15. De Mesquita, B. B., Smith, A., Siverson, R. M., & Morrow, J. D. (2005). The logic of political survival. MIT Press.

16. Dorff, C., Adcox, G., & Konet, A. (2023). Data innovations on protests in the United States. Journal of Peace Research, 60(1), 172–189. https://doi.org/10.1177/00223433221143808.

17. Earl, J., Martin, A., McCarthy, J. D., & Soule, S. A. (2004). The use of newspaper data in the study of collective action. Annual Review of Sociology, 30(1), 65–80.

18. Erden, B., Gamio, L., Lai, K. K. R., Migliozzi, B., Saget, B., Shao, E., & Wu, A. (2025). L.A. protests maps: How immigration raids and federal response unfolded. The New York Times. https://www.nytimes.com/interactive/2025/06/08/us/la-immigration-protests-photos-map.html. 

19. Fisher, D. R., Andrews, K. T., Caren, N., Chenoweth, E., Heaney, M. T., Leung, T., Perkins, N., & Pressman, J. (2019). The science of contemporary street protest: New efforts in the United States. Science Advances, 5(10), eaaw5461.

20. Freelon, D., McIlwain, C., & Clark, M. (2016). Quantifying the power and consequences of social media protest. New Media & Society, 20(3), 990–1011. https://doi.org/10.1177/1461444816676646. 

21. Geise, S., Heck, A., & Panke, D. (2023). “Shiny happy people laughing”: The protest paradigm, WUNC, and the visual framing of political activism. Visual Communication Quarterly, 30(2), 90–105. https://doi.org/10.1080/15551393.2023.2196631.

22. Giugni, M. (1999). How social movements matter: Past research, present problems, future developments. In How social movements matter (Vol. 10, pp. xiii–xxxii).

23. Hatuka, T. (2023). Public space and public rituals: Engagement and protest in the digital age. Urban Studies, 60(2), 379–392.

24. Johnson, A., Ijoma, S., & Kim, D. H. (2022). #MeToo five years later: Progress & pitfalls in state workplace anti-harassment laws. National Women’s Law Center. https://nwlc.org/resource/metoo-five-years-later-progress-pitfalls-in-state-workplace-anti-harassment-laws/.

25. Klein, G. R., & Regan, P. M. (2018). Dynamics of political protests. International Organization, 72(2), 485–521.

26. Lee, F. L., & Chan, J. M. (2016). Digital media activities and mode of participation in a protest campaign: A study of the Umbrella Movement. Information, Communication & Society, 19(1), 4–22.

27. Leong, C., Pan, S. L., Bahri, S., & Fauzi, A. (2019). Social media empowerment in social 

movements: Power activation and power accrual in digital activism. European Journal of Information Systems, 28(2), 173–204.

28. Lohmann, S. (1993). A signaling model of informative and manipulative political action. 

American Political Science Review, 87(2), 319–333.

29. McAdam, D., & Su, Y. (2002). The war at home: Antiwar protests and congressional voting, 1965 to 1973. American Sociological Review, 67(5), 696–721.

30. Motel, S. (2014). Polls show most Americans believe in climate change, but give it low priority. Pew Research Center. https://www.pewresearch.org/short-reads/2014/09/23/most-americans-believe-in-climate-change-but-give-it-low-priority/. 

31. Ortiz, I., Burke, S., Berrada, M., & Saenz Cortés, H. (2022). World protests: A study of key protest issues in the 21st century. Springer Nature.

32. Piven, F. F. (2008). Challenging authority: How ordinary people change America. Rowman & Littlefield.

33. Rossi, L., & Artieri, G. B. (2014). Protest in the digital age. In Media and revolt: Strategies and performances from the 1960s to the present (Vol. 11, pp. 336–349).

34. Roston, M. (2017). The March for Science: Why some are going, and some will sit out. The New York Times. https://www.nytimes.com/2017/04/17/science/march-for-science-voices.html

35. Saad, L. (2017). Concerns about sexual harassment higher than in 1998. Gallup. 

https://news.gallup.com/poll/221216/concerns-sexual-harassment-higher-1998.aspx

36. Shuman, E., Goldenberg, A., Saguy, T., Halperin, E., & van Zomeren, M. (2024). When are social protests effective? Trends in Cognitive Sciences, 28(3), 252–263.

37. Subramanian, R., & Arzy, L. (2021). State policing reforms since George Floyd’s murder. 

Brennan Center for Justice. https://www.brennancenter.org/our-work/research-reports/state-policing-reforms-george-floyds-murder.

38. Tarrow, S. (1991). Struggle, politics, and reform: Collective action, social movements, and cycles of protest. Cornell University Press.

39. The New York Times. (2022). How George Floyd died, and what happened next. The New York Times. https://www.nytimes.com/article/george-floyd.html.

40. Thomas, W., & Ambrose, M. (2017). Congress stands by science in final budget deal – 

AIP.ORG. AIP. https://www.aip.org/fyi/2017/congress-stands-science-final-budget-deal

41. Tilly, C. (1978). From mobilization to revolution. Addison-Wesley.

42. Tilly, C. (1999). From interactions to outcomes in social movements. In How social movements matter (Vol. 10, pp. 253–270).

43. Uluğ, Ö. M., & Acar, Y. G. (2018). What happens after the protests? Understanding protest outcomes through multi-level social change. Peace and Conflict: Journal of Peace Psychology, 24(1), 44.

44. Upadhyay, A., & Trifiro, B. M. (2021). Legislative agenda-setting power of social media: 

#BlackLivesMatter and the George Floyd Justice in Policing Act of 2020. The Agenda Setting Journal, 5(2), 292–313.

45. Wouters, R., & Walgrave, S. (2017). What makes protest powerful? Reintroducing and 

elaborating Charles Tilly’s WUNC concept. SSRN. https://ssrn.com/abstract=2909740.

46. Zweiman, J. (2019). When a hat is not just a hat. The British Museum Blog. 

https://blog.britishmuseum.org/when-a-hat-is-not-just-a-hat/.