The growth of social media platforms is a powerful new instrument for activists and anybody attempting to effect constructive social change. Due to the widespread availability of online forums, activists now have more opportunities than ever to spread awareness about important social issues. As a result, the potential for online discourse to effect positive social change has received much attention in recent years (Farooq. et all) This research aims to better understand the dynamics of online discourse and its impact on social media activism in order to better understand the relationship between discourse and social change. By examining the linguistic strategies, discursive practices, and communication patterns employed by activists in online spaces, we hope to untangle the mechanisms via which online speech contributes to mobilization, awareness, and advocacy for social concerns.

The revolutionary promise of social media lies in its ability to facilitate public discourse, unite people from diverse backgrounds, and create online communities that are not constrained by geography. By combining quantitative research of social media data with a qualitative examination of discourse samples, we hope to provide a comprehensive understanding of how activists use language to frame issues, engage audiences, and motivate collective action (Corinna, G. et al). Understanding the function of language in social media activism requires looking at the linguistic patterns present in social media posts about various social causes. Common narrative devices that have an impact on readers include emotional language, storytelling, and the development of group identities (Walsh, 2020). We will also investigate how activists employ strategies such as hashtags, viral campaigns, and participatory techniques to engage their audiences in meaningful dialogue and motivate them to take action. An essential part of this research is its examination of the impact of online discourse on social change, which involves a thorough examination of how discursive practices on social media platforms translate into offline action, legislative reform, and societal transformation. We will explore the potential role of factors including network structure, community dynamics, and voice amplification in organizing and advancing social movements. Through illuminating the role of speech in this setting, this research hopes to help us better grasp the benefits and drawbacks of online environments for social interaction(M. Chadha,  This study is instructive for activists, organizations, and governments interested in fostering social change through online discussion. There are a number of challenges and ethical issues associated with online activism that this study hopes to illuminate. These include the propagation of misinformation, partisan differences, and algorithmic biases. In a nutshell, this research intends to shed light on the complex interplay between words and clicks in cyberspace. We think that by examining the rhetoric of activists online, we might get insight into how it helps to raise awareness, promote social concerns, and organize people to take action. Ultimately, this research aims to deepen our understanding of the transformative potential of digital media and pave the way for more effective and equitable approaches to social change [7]–[9].

The motivation behind this study was the discovery that the use of social media as a tool for social change was a growing trend. Over the past few years, we've seen numerous examples of how online discourse has been vital in mobilizing communities, bringing attention to important social issues, and bringing about real changes in people's lives and communities [10]-[15]. Because of social media's unique power to bring people together, open lines of communication, and magnify individual voices [16]-[21], activists now have more tools at their disposal than ever before to reach wider audiences and inspire solidarity. Despite the growing significance of online discourse in social media activism, more thorough research is needed to investigate the complex dynamics and methods by which discourse leads to social change [22]-[25]. The goal of this study is to provide you with the information you need to make an informed decision about your business.

While it's generally agreed that social media plays a significant role in fostering social change, there's still a lot to learn about how online conversation contributes to issues like cause mobilization and awareness. Emphasizing social media's widespread reach and quick information distribution, the existing literature frequently focuses on the medium's overall impact on activism. However, there is a lack of research that examines the minute details of online discourse, such as the language tactics, discursive routines, and communicative patterns used by activists. The only way to know if you're doing something right is to look at the results. In order to fully grasp the function of online discourse in social media activism and its potential to effect significant social change, we must solve these research gaps. The research team's goal is to improve the quality of life for all of the participants in the study.

1.      What are the linguistic strategies, discursive practices, and communicative patterns employed by activists in online spaces?

2.      How do activists utilize language to frame issues, engage with their audiences, and foster collective action?

3.      What are the key themes, rhetorical devices, and discursive strategies prevalent in social media posts related to various social causes?

4.      What are the strategies employed by activists to engage their audiences, such as the use of hashtags, viral campaigns, and participatory practices?

5.      How does online discourse on social media platforms contribute to mobilization, awareness, and advocacy for social causes?

According to our hypothesis, activists use particular linguistic tactics, discursive practices, and communicative patterns in their online contacts, which contribute to social cause mobilization, awareness, and advocacy. Our goal in conducting this qualitative research is to learn how activists use language to define problems, connect with their audiences, and inspire solidarity.

§  To identify and analyze the linguistic strategies, discursive practices, and communicative patterns employed by activists in online spaces.

§  To explore how activists, utilize language to frame issues, engage with their audiences, and foster collective action in the context of online discourse.

§  To uncover the key themes, rhetorical devices, and discursive strategies prevalent in social media posts related to various social causes.

§  To investigate the strategies employed by activists to engage their audiences, including the use of hashtags, viral campaigns, and participatory practices, and their impact on mobilization and advocacy.

§  To understand the mechanisms through which online discourse on social media platforms translates into offline action, policy change, and societal transformation, considering the role of network structures, community dynamics, and the amplification of voices.

This study intends to provide a thorough understanding of the function of online speech in social media activism by addressing these research objectives. The outcomes of this study will help us better understand how to use language and other forms of communication in the workplace. The findings of this research will also help activists, groups, and policymakers learn how to use online dialogue to bring about real change in society.

§  This research will contribute to existing knowledge by identifying and assessing linguistic techniques used by activists in online debate. It will reveal how activists use language to frame issues, engage audiences, and promote collective action, revealing effective social change communication tactics.

§  Social media posts about social causes will reveal discursive processes. Studying major themes, rhetorical devices and discursive methods will contribute to a fuller understanding of how activists build narratives and communicate with audiences, highlighting effective mobilization and advocacy strategies.

§  Engagement strategies: This research will examine activists' hashtags, viral campaigns, and participatory techniques to engage audiences. It will show how various strategies affect mobilization and advocacy, revealing ways to promote conversation, active participation, and voice amplification.

§  Understanding the offline impact of online conversation: The study will examine how social media debate leads to offline action, policy change, and societal transformation. It will show how online activism can have real-world effects by addressing network structures, community dynamics, and voice amplification.

§  Policymakers, activists, and organizations: This research will help activists, groups, and policymakers use online conversation to transform society. Based on the discovered linguistic tactics, discursive practices, and engagement strategies, the study will promote more effective and inclusive social media activism.

This paper is organized into five sections as follows:

Introduction: This section will introduce the research issue, discuss online discourse in social media activism, and offer the research objectives and contributions. Literature review on social media activism, online discourse, linguistic techniques, and engagement practices. It will explain how online conversation promotes social change. Methodology: This section describes the mixed-methods research strategy, including data collecting, qualitative discourse analysis, and quantitative social media data analysis. It will explain sample selection and data analysis. The qualitative analysis will show activists' linguistic techniques, discursive practices, and communicative patterns. A quantitative study will examine the prevalence and impact of various linguistic patterns and interaction tactics. Discussion and Conclusion: This section will analyze the findings' ramifications, linking strategies, practices, and mobilization and advocacy. It will include research goals, contributions, and practical advice for activists, groups, and politicians. The section will summarize the study's findings and suggest future research. Table 1 shows the Comparison of different studies.

Table 1

Comparison of Different Studies

The literature on social media's impact on social change and activism sheds light on how digital platforms have altered power dynamics in communication and made it easier for people to take collective action. The impact of social media in promoting social change has been the subject of numerous academic studies [1, for example]. Collective action and social movements in the Middle East and North Africa are studied in [3], and the Arab Spring is used as an example of the impact of social media platforms on these regions. In assessing the effectiveness of #BlackLivesMatter activism through the lens of hashtag activism, [10] examines the benefits and drawbacks of hashtag campaigns. Furthermore, [15] investigates the motivations behind and outcomes of millennials' online activism. Both [18] and [21] explore the potential of social media to help people move up in the world and become more politically engaged. Studies like [24] use critical discourse analysis (CDA) to examine how social media contribute to social movements. The findings of this research add to the knowledge of the power of social media to inspire people to take action on important social issues.

A lot has been written about how social media can be used for social good and action, but there are still questions that need to be answered. While previous study has examined how social media has influenced social movements and activism, more work is needed to understand the complex dynamics of online social activism in diverse geographic areas and cultural contexts. One way to better comprehend the cultural and sociological influences on online activism is to examine the ways in which social media platforms are used in certain locations, such as the Middle East and North Africa. When it comes to the long-term effects and sustainability of social media-driven action, the literature also falls short. Though numerous studies have looked at how social media can be used to organise and bring attention to causes, more inquiry into the long-term effects and transformative power of online activism is warranted. By delving into the link between online activism and real-world social and political change, we may get a fuller picture of social media's power to bring about revolutionary change. Research that analyses the potential pitfalls and obstacles of social media activism is also necessary. Things like the digital gap, algorithmic biases, and the possibility of powerful actors co-opting or manipulating internet platforms should be investigated. Future studies can fill in these blanks and shed light on the intricate web of connections between online activism, social media, and societal transformation if they focus on these areas.

To investigate how online discourse might help bring about societal shifts, this study employs a quantitative approach to social media data. Methods for collecting the data, selecting the samples, and analyzing the results are detailed here.

The method of data collection used is an important part of this study. In order to examine the online discourse and its role in fostering social change, it is necessary to collect the necessary data from social media platforms. This section summarizes the research strategy, the sample population, and the variables used in the data-gathering process.

We used a methodical strategy that included accessing and extracting content from multiple social media platforms like Twitter, Facebook, Instagram, and YouTube to compile the required data. The popularity and sway of these channels in the realm of internet activism led to their selection.

Table 2

Variables Considered in Data Collection

A wide variety of activists, social media users, and organizations working on a wide range of social problems made up the sample population for data gathering. Factors including the influence they have on social media and how relevant their content is to the study's goals were used to determine who would make up the sample population.

Mixed-methods research was used in this study's design; it included both quantitative and qualitative analyses. By taking this method, we were able to gain a deep comprehension of online conversation and its role in bringing about societal shifts.

The data was processed and analyzed utilizing a number of statistical methods for the quantitative analysis. Platform, posts, hashtags, followers, likes, retweets, comments, engagement, and subjects were the main factors analyzed.

Descriptive Statistics: The collected data were summarized and displayed using descriptive statistics. This required the computation of descriptive statistics like mean, median, and standard deviation to illustrate the range and consistency of the data.

The social cause-related social media posts were analyzed using content analysis to uncover common themes, rhetorical strategies, and discursive tactics. Language, emotion, narrative, and the formation of group identities were all examined to arrive at this conclusion.

In order to better understand the community dynamics and network architecture of social media, a network analysis was conducted. This study examined how activists, organizations, and users interact with one another and how their voices are amplified across the network.

The interrelationships and correlations between the variables were explored by statistical analysis. Inferential tests like chi-square and correlation analysis were used to investigate possible links between the variables and spot noteworthy clusters.

By combining these methods of quantitative research, we were able to better understand the dynamics, trends, and patterns of online conversation in social media activism. It offered a numeric basis for knowing how much of a part online speech plays in social cause mobilization, awareness, and advocacy.

In order to examine online conversation and its effect on social media activism, it was necessary to collect relevant data from multiple social media platforms. In this section, we will describe in depth how the data was gathered, what traits and factors were taken into account, and how the dataset was constructed.

With the help of APIs (Application Programming Interfaces) made available by social media sites like Twitter, Facebook, Instagram, and YouTube, we were able to collect the data in an automated fashion. Posts, comments, likes, retweets, and other interaction metrics that have been made public on various platforms can be accessed through these application programming interfaces.

The gathering approach took into account a number of important criteria and aspects to guarantee a thorough evaluation of social media activism's online discourse. Among these variables and characteristics are:

Platform: Which social media site (Twitter, Facebook, Instagram, YouTube, etc.) was mined for information? Posts:  Total number of posts assessed across all platforms; indicative of total content volume.

Hashtags: The posts' hashtags, which are used to group and classify online conversations about social issues.

Followers: Reach and influence can be measured by the number of people who follow the postings' writers.

Likes: The amount of support shown by the audience in the form of likes for the posts.

Retweets: The number of times a tweet was retweeted is a good indicator of how widely it was shared and how much attention it received.

Comments: One reliable measure of a tweet's popularity is the number of times it has been retweeted.

Engagement: An aggregate metric for the level of interaction with the postings, based on a variety of indicators including likes, retweets, and comments.

Topics: The posts' social causes or issues capture the wide variety of topics that activists discuss in internet forums.

Table 3

Overview of Data Collection Parameters and Features

The sample selection procedure involved picking out a statistically-valid subset of the total dataset for deeper examination. This section provides a summary of the sample population as well as an explanation of the selection process and its associated criteria.

To achieve a broad and accurate representation of the population, we used a mixture of random and purposeful sampling methods to pick our sample. The selection of samples proceeded as follows:

To ensure fair and accurate representation, a random sample was initially taken from the complete dataset. To do this, we sampled social media content from each platform at random.

Following the random sample phase, purposive sampling was used to identify advertisements that were found to be most pertinent to the study's aims. To do this, we looked for posts that satisfied a number of criteria, such as having a lot of engagement (by likes, retweets, and comments) and covering a wide variety of themes.

The sample size was established after considering the aims of the study, the resources at hand, and the practicability of the analysis. It tried to find a happy medium between getting enough postings for useful analysis and using up too many resources while doing it.

Iterative Process: An initial set of postings was chosen, and then that set was reviewed and refined over the course of several iterations. This iterative process guaranteed that the final sample included a wide cross-section of content relating to social media activity.

Research goals and the necessity to represent the breadth and complexity of online conversation in social media activism informed the sample selection procedures. The following factors were taken into account when selecting the sample:

Priority was given to posts that addressed social justice, activism, and other topics of societal significance. This made sure that the research's primary topic was reflected in the sample.

Table 4

Overview of Sample Selection Criteria

A representative sample of online conversations about social media activism was used to create this sample population. Posts from a wide range of social media sites, discussing a wide range of social issues and garnering substantial engagement, made up the sample population.

The research objectives called for an examination of the language tactics, discursive practices, and communication patterns used by activists in online forums, and the sample population provided just that.

Researchers used quantitative methods to dissect the chosen sample's use of language, discourse, and communication in online activist communities. The research objectives, the significance of the variables, and the specific quantitative analytic methodologies used are all laid forth in this section.

Language Use: This factor looked at how activists used language in their online discussions, specifically how they appealed to their audience's emotions, told stories, presented evidence, and argued their points. The language we employ is crucial in how we define problems, how we connect with others, and how we come together to take action.

Discourse Patterns: Discourse patterns in social-cause-related social media posts were captured by this variable. Examining activists' use of themes, narratives, rhetorical techniques, and discursive strategies in order to spread their messages and gain support.

Communication Patterns: This variable analyzed the dynamics of activists' interactions with their supporters. Hashtags, viral campaigns, participatory approaches, and other methods used to captivate audiences and inspire them to action were all a part of it.

The purpose of this quantitative study was to shed light on the frequency, trends, and significance of the identified variables in the realm of social media activism. The following were investigated by studying the sample pool:

Frequency and Distribution: A number of language tactics, discursive practices, and communication patterns were analyzed to establish their relative frequencies within the sample. This helped determine the most common strategies used by activists and how they were dispersed among various social movements and online forums.

Engagement Metrics: Likes, shares, comments, and overall audience involvement were examined to determine how the highlighted variables affected these metrics. This made it possible to compare how well various techniques for holding listeners' interest and inspiring them to contribute to the discussion fared.

Comparative Analysis: Quantitative study also entailed contrasting how various social causes, platforms, and activist types made use of the same set of variables. This cross-cultural study illuminated how speakers in various social settings use language, discourse, and communication in distinctive ways.

Correlation and Predictive Analysis: In order to discover possible patterns or links between the variables, a correlation analysis was performed. To further evaluate the potential of various linguistic methods and communication patterns in rallying support and inspiring collective action, predictive analysis techniques like regression analysis and machine learning algorithms can be applied.

Using these methods of quantitative analysis, the study sought to shed light on the ways in which activists exploit language, discourse, and communication to advance social causes and motivate others to take action for positive social change.

Activists' linguistic techniques, discursive practices, and communication patterns in cyberspace were analyzed using descriptive statistics. A table displaying the variables' summary statistics and a thorough explanation of the descriptive statistics used to follow.

In order to better comprehend the central tendency, variability, and distribution of the variables, descriptive statistics provide a description of the sample data. The following descriptive statistics were derived for each independent factor:

Count: The sum count of occurrences where certain language techniques were used.

Frequency: How often each linguistic technique was used?

Percentage: How often each linguistic technique was used?

Count: The sum of the postings with a certain type of content, organization, rhetorical technique, or discursive strategy.

Frequency: How often each type of speech occurs.

Percentage: The share of total discussions that each discourse mode accounts for.

Count: The sum of the occurrences of a given communication pattern (e.g., the use of hashtags, viral campaigns, or interactive methods.

Frequency: How often certain forms of interaction occur.

Percentage: The frequency with which each communication style was observed.

Table 5

Summary Statistics of Key Variables

To calculate the percentage, the following equation was used:

$$\text{Percentage}=\left(\frac{\text{Frequency}}{\text{Count}}\right)*100$$

For example, to calculate the percentage of emotional language used, the equation would be:

$$\text{Emotional Language Percentage}=\left(\frac{\text{25}}{\text{150}}\right)*100=16.7\%$$

These descriptive statistics shed light on the frequency with which certain linguistic tactics, discursive practices, and communication patterns are used by activists in cyberspace, as well as their distribution. They help us comprehend the entire landscape of online discourse in social media activism by providing insights into the frequency of occurrence of specific methods and patterns.

The textual content of social media posts about various social causes was analyzed using content analysis. This section includes a comprehensive breakdown of the content analysis procedure, including a thorough table of results.

Textual data can be subjected to content analysis, which is systematic coding and categorization to reveal recurring themes, topics, and trends. The following procedures were used for content analysis in this study:

Data Collection: Tweets, Facebook status updates, and Instagram photos discussing social issues were compiled. Hashtags and keywords associated with the social issues being studied were used to select the posts.

Coding Scheme Development: Key topics, rhetorical devices, and discursive techniques in the social media posts were identified and classified using a coding scheme. Categories and subcategories were established beforehand; these were obtained from the study's aims and the existing literature.

Coding Process: Social media posts were evaluated and analyzed by trained coders using the coding system. Each comment was read meticulously and given a code that best described the themes, rhetorical strategies, and discursive methods it included. Each post was annotated and tracked using a code that was either assigned automatically or manually by the coders.

Data Analysis: Quantitative analysis was performed on the coded data to establish the frequency with which various themes, rhetorical devices, and discursive strategies were used. The results were summarized using descriptive statistics like counts and percentages.

Table 6

Summary of Content Analysis Findings

To calculate the percentage, the following equation was used:

\text{Percentage}=(\text{Frequency}/\text{Total Number of Posts})*100

For example, to calculate the percentage of social justice themes, the equation would be:

\text{Social Justice Percentage}=(\text{250}/\text{Total Number of Posts})*100

The results of this content analysis shed light on the frequency with which certain themes, rhetorical devices, and discursive strategies appear in social-cause-related posts across different social media platforms. They shed light on the persuasion strategies, narrative tactics, and dominant discourses used by activists in cyberspace.

Online activist communities' structure, dynamics, communication and influence patterns were analyzed using network theory. This section provides a thorough breakdown of the steps involved in doing a network analysis, followed by a table summarizing the results.

Analyzing the connections between nodes in a network, whether they be people, businesses, or social media profiles, is known as network analysis. The following procedures were used for network analysis in this study:

Data Collection: Data was gathered from several social media sites to record the communications and relationships among campaigners. Relationships between followers, the number of retweets, mentions, and replies were all recorded.

Network Construction: Based on the information gathered, a network graph was built, with nodes standing in for activists or groups and edges for the relationships between them. The underlying network diagram was built with the help of some custom software.

Network Measures: The structure and dynamics of the network were analyzed by computing a number of different network measures. To zero in on key players, researchers used centrality metrics including degree and betweenness centrality, as well as the clustering coefficient and network width to gauge the density of interconnections between groups.

Data Analysis: Quantitative analysis of the network metrics was performed to learn more about the network's structure. Findings were summarized and interpreted with the help of descriptive statistics and visuals.

Table 7

Summary of Network Analysis Findings

Degree Centrality: One's degree centrality indicates how influential they are in the network as a whole. The equation allows for its determination:

\text{Degree Centrality}=(\text{Number of Connections of Activist}/\text{Total Number of Activists})

Betweenness Centrality: A node's betweenness centrality reflects how frequently that node appears on the shortest pathways connecting nodes elsewhere in the network. The equation allows for its determination:

\text{Betweenness Centrality}=(\text{Number of Shortest Paths with Activist}/\text{Total Number of Shortest Paths})

Clustering Coefficient: The density of linkages between groups is quantified by the clustering coefficient. The equation allows for its determination:

\text{Clustering Coefficient}=\left(\frac{\text{Number of Connections between Activist's Naghbors}}{\text{Total Number of Possible Connections between Activist's Neighbors}}\right)

Network Diameter: The network diameter quantifies the greatest possible separation between any pair of networked activists.

These conclusions from the network analysis shed light on the most influential activists, information flows, community formations, and the general structure of the online activist network. They are useful for analyzing how people in a network interact with one another and how power is distributed among them.

Relationships and correlations between variables associated with online speech and social media activism are explored using statistical analysis. In this section, you'll detail the statistical methods you used and present a table summarizing your results.

Correlation Analysis: The purpose of correlation analysis is to discover possible patterns or links by analyzing the interrelationships between different variables. It is a statistical tool for gauging the linear relationship between two variables and its direction. The correlation coefficient (r) can take on values between -1 and 1, with values near -1 indicating a strong negative connection, values near 1 indicating a strong positive correlation, and values around 0 indicating either no association or a weak one. The correlation coefficient is determined by the following equations:

r=\frac{(\Sigma \mathrm{[(}X-X_{\mathrm{mean}}\mathrm{)(}Y-Y_{\mathrm{mean}}{\mathrm{)])}}_{}}{(\sqrt{\mathrm{\Sigma }{(X-X_{\mathrm{mean}})}^2}*\sqrt{\mathrm{\Sigma }{(Y-Y_{\mathrm{mean}})}^2})}

A significance level (p-value, for example) can be used to evaluate the statistical significance of the correlation. A statistically significant correlation is indicated by a p-value below a set threshold (for example, 0.05).

Table 8

Correlation Matrix

Figure 1

Regression Analysis: A dependent variable's predictive connection with one or more independent variables can be investigated using regression analysis. It's useful for determining how much the independent factors account for the variation in the dependent variable. When the dependent variable is continuous, linear regression is typically used. The following formula is a linear regression model's simplest form:

$$Y={\beta }_0+{\beta }_1*X+\epsilon$$

When X is the independent variable, Y is the dependent variable, X's connection to Y is represented by coefficient X's, Y's intercept is 0, and the error term is. When there are several different factors to consider, a statistician can use multiple linear regression.

Figure 2

Figure 3

The coefficients of correlation and rho in a correlation matrix are used to quantify the quality and direction of associations between variables. The significance of these associations is measured by their p-values.

Estimated influences of independent factors on the dependent variable are shown as coefficients in the regression analysis table. The importance of these impacts is measured by their p-values.

The quantitative analysis results shed light on the significance of online discourse in mobilizing the public to effect social change through the use of social media. Key findings and their implications for comprehending the dynamics of online activism and the efficacy of various activist techniques are discussed below.

Information was culled from Twitter, Facebook, Instagram, and YouTube, among others, as part of the data collection process. A wide variety of social activists, social media users, and organizations working on a wide range of social issues made up the sample group. Posts that were directly related to social causes, covered a wide variety of topics, and had strong engagement metrics were prioritized in the sample selection process.

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The collected data were summarized and displayed using descriptive statistics. The study uncovered the frequency and distribution of crucial variables associated with the activists' language tactics, discursive routines, and modes of communication. For instance, 16% of the posts contained emotional language, 22% told a tale, 31% used hashtags, and 16% promoted a viral campaign.

Figure 7

Social cause-related social media posts were analyzed using a content analysis tool to uncover common themes, rhetorical techniques, and discursive approaches. Social justice (35.7%), equality (25%), and environmental advocacy (17.1%) were found to be the most prominent topics. Discursive methods like call to action (31%) and storytelling (27.1%) were frequently used, as were rhetorical devices like metaphor (28.6%) and hyperbole (21.4%).

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The make-up and functioning of activist groups in cyberspace were analyzed using a network perspective. Measures of degree and betweenness centrality revealed key players in the study's conclusions. Activist A's 0.12 degree and 0.25 betweenness centralities are indicative of their status as a key node in the network. A clustering coefficient of 0.65 suggests a well-interconnected network of groups.

Analysis of correlations between variables was performed. The results demonstrated a significant positive association between the number of likes and the number of comments (r = 0.70, p 0.001). A moderately positive connection between the number of followers and the number of retweets was discovered (r = 0.45, p = 0.023), indicating that those with a larger number of followers tend to have their tweets rebroadcast more frequently. Engagement, however, did not correlate with the number of posts (r = -0.12, p = 0.357), demonstrating that the number of posts does not significantly affect engagement levels.

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Several crucial implications for comprehending the dynamics of internet activism are highlighted by this study's findings. The importance of emotive communication in captivating audiences and rallying support is demonstrated by the widespread use of emotional language, narratives, and arguments. Understanding how activists frame and convey their messages can be gained via the analysis of dominant themes and discursive methods. Insight into key players and the make-up of online activist communities is provided by the network analysis, which in turn opens doors for strategic partnerships and collaboration. The correlation research also shows that followers play a significant role in amplifying material through retweets, and that likes and comments are crucial in boosting engagement.

These results are helpful in evaluating the impact of online discourse on social change and furthering our knowledge of the quantitative analysis of social media data. This study offers important insights for activists, organizations, and researchers interested in using social media for effective social action by examining linguistic techniques, discursive practices, communication patterns, and network dynamics.