Shaping Public Opinion: The Role of Social Media in Muslim Women’s Empowerment in Uttar Pradesh

The widespread usage of social media platforms has significantly reshaped the communication landscape and plays an essential role in influencing public opinion. These digital platforms have offered new opportunities for women to voice their ideas and promote engagement and participation in public discourse (Alammary, 2022; Gordon & Trammel, 2016; Riquelme et al., 2018). Many researchers have explored the relationship between women and social media, highlighting its role in shaping public discourse and promoting social change. For instance, Mundt et al. (2018) examined the role of social media in promoting social connections, allowing women to form supportive networks and amplify voices that have traditionally been marginalized. Brimacombe et al. (2018) argued that social media platforms can be useful to catalyze women’s participation in socio-political discourse. These platforms have empowered them to engage in digital activism, advocate for their rights and privileges, and mobilize support for collective action, thereby shaping public opinions and challenging dominant traditional societal norms (Mundt et al., 2018).

Şimşek (2012) and Manning (2020) explored the concept of digital storytelling and stated that women use social media to reclaim social agency, share their narratives, and reshape societal perceptions. Through storytelling, women use social media to counter stereotypes, promote inclusivity, and develop a sense of empowerment—ultimately influencing how society perceives gender roles. Furthermore, Brahem and Boussema (2022) and Cesaroni et al. (2017) have investigated the relationship between women, social media, and digital entrepreneurship, highlighting how social media platforms provide entrepreneurial opportunities for women to reshape economic narratives by offering access to marketing, networking, and business expansion. However, despite these positive factors, Herry and Mulvey (2022) and Burke Winkelman et al. (2015) assert that social media may also perpetuate negative stereotypes, as online harassment, cyberbullying, and gender-based discrimination persist in influencing public discourse that can hinder empowerment possibilities.

Empowerment, Muslim Women, and Social Media in the Indian Context

Though definitions of empowerment vary, the major focus is on “identifying capabilities instead of categorizing risk factors and exploring the environmental influences of social problems instead of blaming victims” (Perkins & Zimmerman, 1995). Rappaport (1987) describes empowerment as a process—a mechanism by which people, organizations, and communities gain mastery over their affairs. It promotes growth, development, quality of life, and human dignity (Narayan, 2002). Page and Czuba (1999) mentioned empowerment as a multi-faceted process of gaining control of one’s life. It strengthens the power of an individual to take action on important issues. Women’s empowerment, in particular, is critical for achieving sustainable global development (UNSD, 2020).

Blackwell (1895, p. 28) posits that that “If society will not admit of women’s development, then society must be remodeled.” The term development is often ambiguous and misleading, as language is a powerful weapon (Solnit, 2012). Sen (1999) defines development as a condition that allows freedom by opposing injustice and discrimination. Chambers (1997) describes it as a positive transformation—moving to a state that betters the prior circumstance. The impact of women’s empowerment extends beyond individual benefits; it transforms societal perspectives. Bayeh (2016) highlights how women’s empowerment is essential for cohesive national development, while Mohanty (2016) reinforces that development is both authoritative and indispensable for societal and national progress.

Understanding the relationship between women, empowerment, and social media within India’s diverse socio-political and religious landscape is a complex yet significant area of investigation (Sangari, 1995). The Muslim community, India’s largest and most significant religious minority, has faced consistent economic and socio-political challenges, as documented by the Sachar Committee (Jaffrelot & Gayer, 2012; Jahan, 2016; Robinson, 2008). Factors such as low literacy rates and high unemployment contribute to economic vulnerability, disproportionately affecting Muslim women by limiting their educational and employment opportunities (Robinson, 2008). In such contexts, empowerment through awareness and consciousness-raising becomes essential for reshaping public perceptions and driving social change (Waitoa et al., 2015). According to Paulo Freire’s concept of critical awareness, recognizing sociopolitical issues and understanding the power of collective voice can lead to transformative empowerment and systemic change (Waitoa et al., 2015).

Against this backdrop, social media has become an important way for Muslim women to engage in public discourse. Digital platforms like X (Twitter), Facebook, and Instagram promote digital activism, education, and networking, creating new avenues for empowerment. Campaigns such as #EducateMuslimGirls and #ShaheenBaghProtests have mobilized political engagement (Ali, 2024), while movements like #HijabRow have highlighted struggles for religious and educational rights (Joseph & Raveendran, 2025). However, active participation in socio-political discourse online often exposes Muslim women to targeted harassment, impeding their access to and involvement in digital public spaces (Hirji, 2021). Politically active and highly vocal Muslim women frequently face backlash, as seen in incidents like Sulli Deals and Bulli Bai, where morphed images of Muslim women were shared for online “auction” in a misogynistic and communal act of harassment. These incidents highlight the systemic digital oppression they face, revealing how social media, while serving as a tool for empowerment, also subjects them to targeted cyber abuse (Bhimdiwala et al., 2024).

Theoretical Framework

Critical consciousness, or conscientização, originated from Paulo Freire’s work and popular education. Freire urged illiterate and underprivileged people to question the political and social inequities eternally perpetuated by oppressive systems. He argued that marginalized groups could initiate change only if they critically understood poverty, injustice, exploitation, history, and economics. Raising awareness about social and economic injustices enables marginalized people to challenge oppressive systems and advocate for change (Freire, 1974). According to Freire, critical consciousness combines reflection and action to transform social systems and conditions. Freire and Macedo (1998) define critical literacy as the ability to read written words and interpret and critique the world around them. Critical consciousness has influenced various theoretical frameworks and studies in education, community psychology, and communication (e.g., Cammarota & Fine, 2008; Fisher, 2007; Prilleltensky, 2012; Souto-Manning, 2010; Varas-Díaz & Serrano-García, 2003).

The relationship between women, social media, and public opinion can be understood through Freire’s theory of critical consciousness. Freire highlights the interrelationships between social justice, education, and social change, arguing that individuals must collectively mobilize and act as change agents to influence societal narratives and bring about transformative change (Ledwith, 2015). In the digital age, social media has emerged as a powerful tool for shaping public opinion by promoting critical consciousness among women (Xiao, 2020). Freire’s concept of critical consciousness involves learning to perceive and act against social, political, and economic challenges (Diemer et al., 2016; Jemal, 2017). Social media platforms allow women to share their stories, challenge dominant discourses, and engage in discussions that shape public perceptions of gender inequalities (Swank & Fahs, 2017; Xiao, 2020).

Women acting as opinion leaders on social media redefine their social agency and influence the broader public discourse (Chiluwa, 2021; Miladi, 2016). It aligns with Freire’s view that critical consciousness requires active engagement, empowering individuals to recognize injustices and their potential as a unified force against inequality and social justice (Diemer et al., 2016). Freire’s concept of liberatory practices extends to critical engagement with media and participating in socio-political debates as informed citizens (Farag et al., 2021; Mayo, 1995; Singh, 2008). In shaping public opinion, liberatory practices manifest through the active involvement of women in online spaces, where they can bring new levels of awareness to issues affecting them and others (Douglas et al., 2016; Keller et al., 2018). Social media, as a digital public space, allows women to influence public opinion by sharing experiences, mobilizing support, and promoting discourse that contributes to political and social change—aligning with Freire’s vision of engaged citizens leading to transformative action (Al’Uqdah et al., 2019; Campbell, 2014; Sobieraj, 2018).

Despite the lack of extensive empirical research on the impact of social media on critical consciousness, the assertion is made that social media can shape public opinion and develop critical consciousness that leads to social action and subsequent social justice. While critical consciousness existed before the advent of social media, the digital realm enhances its potential for information dissemination, dialogue, and collective mobilization (Xiao, 2020). Analyzing the relationship between social media and critical consciousness contributes to a broader understanding of how digital spaces shape public opinions, influence attitudes, and contribute to social change, especially among Muslim women.

Social Media Engagement and Public Opinion Formation

Social media engagement influences public opinion and critical consciousness, particularly among marginalized groups. People use social media for various purposes, from passive information consumption to active content creation (Li, 2016; Muntinga et al., 2011). Participating in online discussions and producing digital content shapes narratives around gender, identity, and socio-political issues (Knapp et al., 2010; SDRC, 2014). Previous studies have shown that active social media users, particularly those engaged in content creation, experience higher psychological and social empowerment levels (Leung, 2009; Petrovčič & Petrič, 2014). However, the relationship between gender, religion, and digital participation remains underexplored, particularly in the Indian context. Understanding how Muslim women engage with social media and use it for empowerment is critical to examining its broader societal impact (Parveen & Gouda, 2022).

Social media has proven effective in mobilizing social movements and influencing public discourse. Movements like #MeToo and #BlackLivesMatter have demonstrated how digital platforms can amplify marginalized voices and create positive social change (Carney, 2016; Clayton, 2018). In India, women’s engagement with social media has increased awareness of gender issues, community mobilization, and socio-political advocacy. Despite the challenges posed by misinformation and online hate speech (Gurgun et al., 2024), digital spaces remain important for promoting critical dialogue and enabling women to shape public opinion on their rights and empowerment (Kamau, 2017; Schuster, 2013).

By analyzing social media engagement and its impact on Muslim women’s empowerment in Uttar Pradesh, this study contributes to the ongoing discourse on digital activism, gender equality, and socio-political participation in contemporary India.

Research Method

The critical conscious conceptual model based on Paul Fairie’s theory used in this study focuses on the interrelation of women, social media, and social empowerment, explicitly focusing on Muslim women. The model incorporates an engagement typology inspired by Muntinga et al. (2011), as depicted in Figure 1. The independent variable—social media engagement behavior—encompasses consumption, contribution, and creation, based on the theoretical constructs of Muntinga et al. (2011). The dependent variable, social empowerment, comprises four dimensions: informational assets, social agency, social capital, and social impact. Informational assets measure the acquisition of valuable knowledge; social agency evaluates the ability to take action and influence society; social capital explores networking and social connections; and social impact measures broader societal changes resulting from engagement.

A structured questionnaire was developed to collect empirical data, including indicators that capture the nuances of social media engagement behaviors and social empowerment. The questionnaire consists of nine statements analyzing social media engagement behaviors and twenty-one items assessing various aspects of social empowerment. The primary aim of this study is to investigate and understand the relationship between social media engagement behaviors and the social empowerment of Muslim women within the context of social media. By employing the critical consciousness theoretical framework, we offer nuanced insights into how Muslim women engage with social media and how their engagement contributes to their social empowerment across different dimensions.

Figure 1.Conceptual framework

Hypotheses

Based on the literature, the following major assumptions are made:

H1: Social media consumption is positively related to social empowerment.

H1a: Social media consumption is positively related to using it as an informational asset.

H1b: Social media consumption is positively related to social agency.

H1c: Social media consumption is positively related to social capital.

H1d: Social media consumption is positively related to social impact.

H2: Social media contribution is positively related to social empowerment.

H2a: Social media contribution is positively related to the informational asset.

H2b: Social media contribution is positively related to social agency.

H2c: Social media contribution is positively related to social capital.

H2d: Social media contribution is positively related to social impact.

H3: Social media creation is positively related to social empowerment.

H3a: Social media creation is positively related to the informational asset.

H3b: Social media creation is positively related to social agency.

H3c: Social media creation is positively related to social capital.

H3d: Social media creation is positively related to social impact.

Measurements

We used the social empowerment scale and the social media engagement behavior scale.

Social Media Engagement Behavior

Social media engagement behaviors include consumption, contribution, and creation. The sub-dimensions of consumption include view, which is the passive behavior of users in which they watch content on social media, and read, which is the engagement of social media users in reading content on social media. Contribution has sub-dimensions such as like, which represents users’ appreciation of social media content; dislike, which refers to users’ behavior of dissatisfaction with the content; comment, which is the engagement behavior of social media users in expressing opinions about the content; and share, which means making content available to the users, friends, or networks. Creation includes dimensions such as upload, which is active social media engagement that involves uploading information on social media sites.

Social Empowerment

Social empowerment has been constructed as a higher-order formative construct involving four dimensions, i.e., social agency, social capital, informational assets, and social impact:

Informational asset: It refers to access to information related to social issues.

Social agency: Exercising choice through observable and unobservable actions.

Social capital: it refers to getting mutual benefits and cooperation from generalized reciprocity, social trust, and networks.

Social impact: It is an individual’s influence on society.

Sampling and Data Collection

The data was collected from the Bahraich district of Uttar Pradesh. According to the 2011 Census of India, Muslims constitute approximately 33.53% of Bahraich’s population, indicating a substantial Muslim presence in the district (Census of India, 2011). Furthermore, Bahraich is identified as one of India’s most socio-economically challenged districts, with 55% of its population classified as multidimensionally poor according to NITI Aayog’s Multidimensional Poverty Index (Pavithra, 2023). The district is divided into four sub-districts, or tehsils: Bahraich, Kaiserganj, Nanpara, and Mahasi (ViewVillage, 2023). A survey of representative samples provides a quantitative explanation of the characteristics and opinions of the population (Creswell, 2009). A survey method was employed to get information from Muslim women’s social media users. A survey was conducted using a structured questionnaire to achieve the study’s objective, i.e., to examine the relationship between social media engagement behaviors and social empowerment among Muslim women. Data collection took place between March and April 2024, focusing on Muslim women in the Bahraich district who had been using social media for at least one year and were over the age of 18. Participants were required to complete the questionnaire offline, which took approximately 15 to 20 minutes. A multi-stage cluster sampling method was employed to ensure a representative sample. Specifically, a three-stage cluster sampling approach was used, where random selection was applied at each stage, progressing from larger clusters to smaller units. In the first stage, four tehsils from Bahraich—Nanpara, Mahasi, Bahraich, and Kaisarganj—were selected. In the second stage, simple random sampling was used to choose villages or towns within each tehsil, ensuring proportional representation of the population. To ensure balanced representation of both urban and rural populations, two villages and one town were selected from Nanpara and Kaisarganj while one village and one town were chosen from Bahraich. Since Mahasi lacks an urban population, two villages were selected from this tehsil. In the final stage, 400 Muslim women were randomly selected from households along the streets of the chosen villages or towns, ensuring diversity in the sample.

Sample Size Determination

A sample size of 400 was selected for data collection, exceeding the minimum required sample size calculated using GPower 3.1 software. With seven predictors, the minimum sample size found by GPower 3.1 (Faul et al., 2007, 2009) was 74. This was because the medium effect size (f²) was 0.15, the probability of error was 0.05, and the statistical power (1 - β error probability) was 0.95.

Additionally, the minimum sample size was estimated using the 10-times rule method (Hair et al., 2011), which involves multiplying 10 by the highest number of indicators or items in any construct. With a maximum of six indicators, the required sample size was 60.

Furthermore, we used Taro Yamane’s formula (Yamane, 1973) to calculate the sample size:

$$n=\frac{N}{1+N(e)^2}$$

Where:

n = Sample Size

N = Total Population

e = Margin of Error (Confidence Level)

Given the total population (N) of 90,739 and a margin of error (e) of 0.05, the formula yields:

$$n = \frac{90,\ 739}{1\ + \ 90739\ (0.05²)}$$

$$= 400$$

Thus, a final sample size of 400 was chosen to ensure statistical robustness and representativeness.

Measurement Model Assessment

Content Validity

The scale has gone through thorough content validity by calculating the content validity index at the item level (I-CVI) (Lynn, 1986), scale-level content validity index (S-CVI)/average (Polit et al., 2007) and modified kappa (K* scores) (Fleiss, 1981). The social media engagement scale had an I-CVI greater than .08, an S-CVI/AVE greater than .91, and a K* greater than or equal to .76. The social empowerment scale had an I-CVI higher than .78, an S-CVI/AVE greater than .90, and a K* greater than or equal to .85. This shows that both scales have excellent content validity.

Common Method Bias

The study adhered to human ethics as defined by the Central University of Tamil Nadu’s Institutional Human Ethics Review Board (IHERB). Respondents were guaranteed the privacy and confidentiality of the data gathered from them throughout data collection. Furthermore, to ensure voluntary participation, a consent form was included with the questionnaire, and the questionnaire was translated into the local language. Common method bias is a phenomenon that occurs when the relationship between two or more constructs is distorted as they were measured using the same method (Podsakoff & Organ, 1986). Common method bias can arise due to uniformity in responses, respondents’ emotional states, and social desirability bias (Jordan & Troth, 2019; MacKenzie & Podsakoff, 2012). Common method bias can also arise if similar formats or wordings of the items are used in a questionnaire that creates uniform responses (Jordan & Troth, 2019). We also used a quantitative metric, Smart PLS-SEM, to check for common method bias (Kock, 2015). The results indicate that the variance inflation factor (VIF) values were less than 3.3, suggesting they were below the threshold value (Kock, 2015). As shown in Table 1, the data show no evidence of common method bias.

Results

SmartPLS software version 4, has been used to analyze the collected data. To measure the quality of the constructs, the measurement model of reflective constructs (consumption, creation, contribution, social agency, social capital, informational asset, and social change) was tested. The measurement model examines the relationship between constructs and associated indicators. Measuring the validity and reliability of scales is important because they were both constructed, including the social empowerment scale and the engagement behavior scale (consumption, contribution, and creation).

The measurement model for reflective constructs was analyzed in the following ways:

1. Indicator reliability

2. Internal consistency reliability

3. Convergent validity and

4. Discriminant validity

Table 2 indicates assessment of the indicator reliability includes measuring the factor loadings. Indicator loadings of more than .708 are recommended, as the construct expresses an indicator variance of more than 50% (Hair et al., 2019). Indicator loadings for all the variables exceeded the minimum threshold value of .708 at the 5% significance level. Internal consistency reliability has been measured by Cronbach’s alpha and composite reliability (Hair et al., 2019). Composite reliability values between .70 and .90 are recommended (Hair et al., 2019).

It can be seen in Table 2 that both Dijkstra & Henseler’s rho (A) and Cronbach’s alpha coefficient values are higher than the minimum value of .70 recommended by Hair et al. (2019). This means that the data is internally consistent and reliable. Measuring the convergent validity of each construct is the third step in assessing the measurement model. The Average Variance Extracted (AVE) measures the convergent validity. Convergent validity is demonstrated by AVE values greater than .50 (Fornell & Larcker, 1981; Hair et al., 2019). Table 2 shows that the AVE values were between .60 and .78. Therefore, the AVE values fell above the cutoff points suggested by Hair et al. (2019), establishing convergent validity. The next step is to measure the discriminant validity of the constructs. The Fornell-Larcker criterion and Hair et al.'s (2019) Hetero-trait-Monotrait (HTMT) criteria were used to measure discriminant validity.

The model fit can be checked using a standardized root mean square residual (SRMR) (Hu & Bentler, 1999). SRMR is the basic model fit criteria in PLS-SEM (Hu & Bentler, 1998). After examining the common method bias and measurement model, the model fit was examined using SRMR, which was found to be .06, which is under the required threshold value of 0.08 according to Hu and Bentler (1999).

As seen in Table 3, the square root of AVE (bolded) in the diagonal was higher than the values in the off-diagonal elements that represented the connections with other constructs. Regarding HTMT, Henseler et al. (2015) suggested a threshold value of less than .85 for conceptually distinct concepts (Henseler et al., 2015). The result of HTMT was found to be satisfactory, as shown in Table 4. The measurement model results of reflective constructs are presented in Table 2.

Structural Model Assessment

The structural model, or inner model, measures the relationship between latent constructs (Hair et al., 2017). For assessing the structural model, the following requirements have been fulfilled:

1. Evaluating collinearity issues in the structural model

2. Model’s explanatory power

3. Model’s predictive power and

4. The statistical significance and relevance of the path coefficients (Hair et al., 2022).

The variance inflation factor (VIF) values were used to ensure that there was no bias in the regression results (Hair et al., 2019). According to Hair et al. (2017), the optimal VIF values are less than or near to 3. The study’s VIF values were less than 3, as shown in Table 5. As a result, it was determined that there was no multicollinearity in the data.

The second step is to measure the explanatory power of the model, which includes coefficients of determination (R²), also known as in-sample predictive power (Rigdon, 2012). It assesses the variance in the endogenous constructs caused by exogenous constructs (Hair et al., 2019). To explain the predictive accuracy, R² ranges between 0 and 1, whereas R values of .75, .50, and .25 are considered substantial, moderate, and weak (Henseler et al., 2009). As shown in Table 6, the R² values were above .50; thus, the model has predictive power. We may argue that social media engagement behavior, i.e., consumption, contribution, and creation, explains 40%, 43%, and 48%, of the change in informational assets, social agency, and social capital, respectively, whereas 51.3% of the change in social media engagement is explained by social impact, indicating the “moderate” predictive power of the construct. We measured F to determine how the exogenous variable’s omission from the model affects R. F² is also known as effect size. The effect size is weak, moderate, and robust if the values are 0.02, 0.15, and 0.35, respectively (Cohen, 1988). The effect size of contribution was strong for social capital (0.34), moderate for social agency (0.22) and weak for informational asset (0.08) and social impact (0.04). This finding implies that the removal of contribution will significantly influence social empowerment (social capital and social agency). However, the effect size of consumption was weak for informational assets (0.09) and social agency (0.02). In creating behavior, only social impact (0.30) has a moderate effect size. Other factors can also affect social empowerment, given its multi-dimensional nature.

The results are reported in Table 6. Stone-Geisser’s (Q²) is another criterion for determining the model’s predictive power, also termed as out-of-sample predictive relevance of the model. Q² values show the predictive relevance of the model in explaining the outcome variables (Cohen, 1988). Q² values more than 0 indicate predictive power (Hair et al., 2019), while values of 0.02, 0.15, and 0.35 indicate small, medium, or large predictive relevance, respectively (Hair et al., 2020). The results revealed that Q² values were greater than zero, with Q² values for informational assets, social agency, social impact, and social capital being 0.39, 0.42, 0.50, and 0.47. As a result, the model has a high predictive relevance (Table 6).

Following the model’s predictive capability, the next criterion is to assess the model’s predictive power. Shmueli et al. (2016) introduced PLSpredict to determine the predictive capability of the model. It is known as out-of-sample predictive power. The root-mean-square error (RMSE) is the most commonly used metric to assess the predictive power of a model. The prediction errors were symmetrically distributed, so RMSE values were checked. RMSE values were compared with the linear regression model (LM) benchmark. When compared, the results indicated that the errors in PLS-SEM_RMSE were more than the LM_RMSE values for all the indicators. As a result, the model has high predictive power.

Hypotheses Testing

The last step was to use a bias-corrected and accelerated (BCa) bootstrap procedure with 5000 sub-samples and a 95% confidence level to check if the relationships in the structural model were significant. As per the objective of the study, the results revealed that social media engagement has a statistically significant and positive impact on social empowerment.

Social media engagement behaviors are significantly related to all four dimensions of social empowerment. The findings support H1a, H1b, H1c and H1d, e.g., Consumption -> Informational Asset (β = .31, p < .001), Consumption -> Social Agency (β = .16, p = .005), Consumption -> Social Impact (β = .13, p = .003) and Consumption -> Social Capital (β = .12, p = .008). The findings also support H2a, H2b, H2c and H2d. For instance, Contribution is significantly related to Informational Asset (H2a: β = .30 p < .001), Social agency (H2b: β = .49, ρ < .001), Social Impact (H2c: β = .19, p < .001) and Social Capital (H2d: β = .58, p < .001). The study find support for H3a, H3b, H3c and H3d.Creation is significantly related to Informational Asset (H3a: β = .12, p = .010), Social Agency (H3b: β = .085, p = .044), and Social Impact (H3c: β = .495, p < .001) but not significantly related to Social Capital (H3d: β = .049, p = .146). Therefore, all hypotheses are supported except H3d, as shown in Table 7.

Discussion

The interrelation of women and social media, particularly among Muslim women in India, provides a critical framework for understanding how digital engagement promotes critical consciousness and influences public discourse that challenges social inequalities. Based on Paulo Freire’s theory of “critical consciousness,” which emphasizes education, social justice, and collective action, this framework is particularly relevant for investigating the positions of marginalized groups. Muslim women in India are increasingly using social media platforms to amplify their voices, critically engage with socio-political issues, and shape public narratives. However, statistical data suggests significant digital disparities that impact their engagement. According to NFHS-5 (2019-21), only 33.3% of women in India have used the Internet, compared to 57.1% of men. The gender gap is more pronounced in Uttar Pradesh, where only 20.6% of women have Internet access. Additionally, the Pew Research Centre (2021) highlights that Muslim women in India face greater digital exclusion than women belonging to other religions due to sociocultural restrictions and lower literacy rates.

A study conducted in the Bahraich district of Uttar Pradesh emphasizes that social media can be a transformative tool for promoting social empowerment among women. It highlights that engaging behaviors—consumption, contributions, and content creation—are key to influencing empowerment outcomes and public narratives. This aligns with the measurement model assessment, which found that social media engagement behaviors explained a significant portion of the variance in empowerment-related constructs. Specifically, engagement behaviors accounted for 40.1% of the variance in informational assets, 43.1% in social agency, 47.9% in social capital, and 51.3% in social impact. These findings suggest that digital engagement can be an important tool for social empowerment, particularly among marginalized women.

Passive content consumption, which includes reading posts, watching videos, and participating in discussions, provides women with valuable information about social issues and enhances their critical engagement. For example, movements such as #MeTooIndia and #JusticeForAsifa have raised awareness about gender justice and encouraged women to speak out against violence. The statistical analysis confirmed that content consumption has a significant positive effect on informational asset (β = 0.31, p < .001) and social capital (β = 0.12, p = .008), supporting the role of social media in shaping knowledge and promoting social networks. However, limited digital literacy often restricts broader participation. The GSMA (2019), Mobile Gender Gap Report found that women in India are 28% less likely than men to own a smartphone, which significantly affects their ability to engage with digital content.

Active engagement behaviors, such as liking, sharing, and commenting, allow women to amplify their voices and challenge dominant discourses. For instance, social media campaigns like #PinjraTod encouraged women to contest patriarchal norms in educational institutions. The Lokniti-CSDS report (2022) found that women who actively use social media are 1.5 times more likely to discuss political issues compared to those who do not. The report emphasizes that engaging in digital conversations enhances women’s ability to engage with sociopolitical issues. However, digital participation remains skewed towards urban and educated women, with lower representation from rural and marginalized groups.

Content creation is a powerful tool for reshaping public discourse. By producing original content, women can challenge dominant narratives and mobilize support for social causes. Movements such as #GirlsAtDhabas have successfully brought attention to women’s right to public spaces. The measurement model confirmed that content creation has a moderate effect on social impact (f² = 0.303), suggesting that producing digital content significantly contributes to shaping social narratives. However, while content creation enhances visibility, it does not always translate into wider influence or social capital. The model’s predictive power assessment (Stone-Geisser’s Q² values ranging from 0.39 to 0.50) highlights that engagement behaviors predict empowerment outcomes, but collaborative networks and institutional support are needed to sustain long-term impact.

Despite these opportunities, multiple barriers limit Muslim women’s participation in digital discourse. Limited digital access remains a significant hurdle, as NFHS-5 data suggests that only one in five women in Uttar Pradesh has Internet access, restricting their engagement. Socio-cultural constraints also play a role, as many Muslim women face family restrictions on social media usage, limiting their ability to participate in online debates. Additionally, the GSMA (2019) report highlights that Indian women are less likely to be trained in digital literacy, reducing their ability to create and share content effectively. The measurement model confirmed that digital participation is significantly influenced by access and self-efficacy, reinforcing the need for targeted interventions to bridge these gaps.

Social media engagement plays a crucial role in shaping public opinion and empowering marginalized women. Consumption, contribution, and content creation are key pathways for critical consciousness, allowing women to challenge societal norms and advocate for social change. The statistical analysis confirmed that these engagement behaviors significantly contribute to social empowerment, with all the path coefficients statistically significant (p < .05). However, to maximize the potential of digital platforms, efforts must focus on increasing digital literacy, expanding access to technology, and addressing cultural barriers. By incorporating statistical data and empirical studies, we gain a more profound understanding of how social media influences public discourse among Muslim women in India. While digital platforms offer a space for empowerment, structural inequalities must be addressed to ensure equitable participation in shaping public narratives. A theoretical model was developed based on this analysis, presented in Figure 2, illustrating the channels from social media engagement to social empowerment among Muslim women in India.

Figure 2.Social Media Engagement to Social Empowerment

Conclusion

This study examines social media engagement behaviors and their impacts on social empowerment among Muslim women in Bahraich District, Uttar Pradesh. It highlighted the influence of social media engagement—encompassing consumption, contributions, and content creation—in promoting various dimensions of empowerment, such as social impact, social capital, information assets, and exercising social agency. Active engagement, specifically through contributions, was identified as a key factor in raising awareness, building networks, and challenging stereotypes and oppressive social norms. Women’s active participation on social media enables them to share their ideas and beliefs, which may influence others, trigger engagement in discussions on critical social issues and mobilize support for social change. The findings emphasize the transformative potential of social media for advancing women’s empowerment while also indicating a need to address barriers that restrict women’s online participation.

The study provides insights into how social media can support women’s empowerment by encouraging active engagement, improving media literacy, and enhancing digital skills. It highlights the importance of targeted interventions by policymakers, organizations, and researchers for understanding the power of social media to promote public discourse that encourages inclusive empowerment and positive social change.