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Activism Through the Eyes of Social Media

How has social media changed the way people organize activism in real life (IRL)? Social media allows activists to quickly share information, gather supporters, and coordinate events in real time. Hashtags like #BlackLivesMatter or #MeToo help unify people across geographic distances, making it easier to turn online discussions into large-scale protests and community actions.

What is one advantage of using hashtags for activism? Hashtags create visibility. When thousands of users post with the same tag, it boosts awareness and encourages more participation. This visibility can force public institutions, politicians, or corporations to respond to the issue being raised.

What is one criticism of hashtag activism? Some critics argue that hashtag activism encourages “slacktivism”, where people support a cause by posting about it online but don’t take real-world action. This means awareness may spread, but meaningful change requires more than social media participation.

How do social media platforms help marginalized communities in activism? Social media gives marginalized groups a voice that traditional media often overlooks. It allows people to share personal experiences, document injustices, and gather support without relying on mainstream news outlets, which has led to greater recognition of systemic issues.

Sources;

Freelon, D., McIlwain, C., & Clark, M. (2016). Beyond the Hashtags: #Ferguson, #BlackLivesMatter, and the Online Struggle for Offline Justice. Center for Media & Social Impact.

Tufekci, Z. (2017). Twitter and Tear Gas: The Power and Fragility of Networked Protest. Yale University Press.

#gsskittens #sociology

Platform Politics through Social Media

Question 1: How do social media algorithms influence political opinions? Social media algorithms, most of the time, prioritize content that increases engagement, which often favors emotionally charged or divisive posts. This can create “echo chambers,” where users only see views that confirm their beliefs, deepening polarization. For example, Facebook’s algorithm was criticized for promoting misleading political content during the 2016 U.S. election (Allcott & Gentzkow, 2017). As users interact with posts they agree with, the system reinforces those biases, shaping political identity and behavior. Algorithmic design thus shifts political discourse from fact-based discussions to emotionally driven reactions, which can distort democratic decision-making.

Question 2: What role does content moderation play in shaping political discourse? Content moderation determines what speech is visible online, giving platforms immense political power. While moderation helps prevent misinformation and hate speech, it can also silence marginalized voices or controversial opinions. During movements like #EndSARS or #FreePalestine, some users reported post removals, sparking accusations of political bias. Balancing free expression and harm prevention remains a challenge. The lack of transparency in how content is flagged or removed makes it difficult for users to trust the fairness of online discussions, especially when politically sensitive issues are involved (Tufekci, 2015).

Question 3: How has “trending news” shaped public perception of political events?

“Trending news” features amplify specific narratives, sometimes distorting which issues appear most important. For example, X (formerly Twitter) can make minor political controversies seem significant while sidelining more substantial but less viral stories. This selective visibility influences public attention and media coverage. In political campaigns, candidates’ trending moments, which are positive or negative, can heavily alter voter perceptions overnight. As a result, what becomes “newsworthy” is less about truth or relevance and more about engagement metrics, reducing politics to entertainment rather than informed civic understanding (Tufekci, 2015).

Question 4: How do bots and misinformation campaigns manipulate online political discourse? Automated bots and troll farms have been used to flood online platforms with false or biased information, aiming to influence elections or shape public opinion. During the 2020 U.S. election, researchers discovered coordinated disinformation networks disseminating conspiracy theories to erode trust in democratic institutions (Vosoughi et al., 2018). Bots can artificially amplify certain hashtags, making fringe ideas appear mainstream. These tactics exploit social media’s virality and users’ limited ability to fact-check. Ultimately, such manipulation weakens democratic dialogue by replacing genuine debate with noise and confusion, thereby eroding trust in both the media and the political system.

References:

Allcott, H., & Gentzkow, M. (2017). Social media and fake news in the 2016 election. Journal of Economic Perspectives, 31(2), 211–236. https://doi.org/10.1257/jep.31.2.211

Tufekci, Z. (2015). Algorithmic harms beyond Facebook and Google: Emergent challenges of computational agency. Colorado Technology Law Journal, 13(1), 203–218.

Vosoughi, S., Roy, D., & Aral, S. (2018). The spread of true and false news online. Science, 359(6380), 1146–1151. https://doi.org/10.1126/science.aap9559

#gsskittens #sociology

Gender Influence Through Social Media

1. How does social media shape the way people understand and express gender identity?

Social media allows people to explore and express diverse gender identities while connecting with supportive communities. However, it also reinforces stereotypes about how masculinity and femininity should appear. This creates a space of both freedom and pressure, where self-expression can thrive but also conform to cultural norms.

2. In what ways do influencers and online trends reinforce or challenge traditional gender roles?

Influencers can challenge gender norms by promoting inclusive fashion and body positivity, yet many still reinforce traditional roles through appearance-focused or dominance-based content. Viral trends often amplify gender expectations by rewarding stereotypical behavior. Thus, social media simultaneously challenges and maintains old gender ideals, depending on who and what goes viral.

3. How might social media platforms contribute to gender stereotypes through their algorithms and content recommendations?

Algorithms often promote gendered content that receives high engagement, unintentionally reinforcing stereotypes about beauty or masculinity. When users interact with such posts, platforms push similar material, creating echo chambers of gender norms. This repetition normalizes narrow gender roles and influences how people view themselves and others online.

4. What impact does social media have on young people’s self-image and confidence in relation to gender expectations?

Social media affects self-image by promoting idealized gender standards. Girls often face pressure to look perfect, leading to lower self-esteem, while boys may feel compelled to appear strong or emotionless. Gender-diverse youth find both support and discrimination online. Overall, constant comparison and validation cycles can weaken confidence.

#gsskittens

Digital Identities

1. In what ways do profile pictures, avatars, or handles function as “digital self-portraits,” and what do they communicate?

Your profile pictures, avatars, and handles act as digital self-portraits that communicate aspects of your personality, interests, and identity to others in online spaces. Because digital interactions lack many physical cues, these elements become symbolic markers of how you wish to be seen. For instance, a professional headshot on LinkedIn projects competence, while a cartoon avatar on a gaming site may express humor or creativity. Scholars note that online identity is a “configuration of defining characteristics” constructed and selectively presented to fit different digital contexts (Frontiers in Psychology, 2021). This self-presentation is both intentional and revealing, showing how individuals balance authenticity and impression management when curating their online selves (Frontiers in Psychology, 2021).

2. Do people maintain distinct digital “selves” across different platforms?

Most people maintain multiple digital “selves” across platforms because each space has different social norms and purposes. On professional sites like LinkedIn, users tend to emphasize career achievements and expertise, while on gaming or dating platforms, they may highlight humor, hobbies, or attractiveness. Research shows that individuals tailor their language, photos, and overall tone to align with the expectations of each audience, creating context-specific versions of themselves (ArXiv, 2017). The benefit is flexibility by being able to express different dimensions of identity safely, but the trade-off is fragmentation, as one’s online self becomes a collection of partial personas that can feel disconnected when viewed together (ArXiv, 2017).

3. How might discrepancies between one’s “digital identity” and your offline self affect your sense of authenticity or mental well-being?

When your digital identity differs significantly from your real-world self, it can lead to self-discrepancy, a psychological gap that may cause stress, anxiety, or lower self-esteem. Studies show that large differences between a person’s digital and offline identities are linked to negative emotional outcomes because maintaining a curated or idealized persona requires constant effort and comparison (ResearchGate, 2024). However, some level of experimentation online like such as trying out new interests or aesthetics, can be healthy and help people explore who they are in low-risk environments. The key is finding balance: embracing the freedom of digital spaces while ensuring your online identity still reflects core aspects of your authentic self (ResearchGate, 2024).

4. What role do anonymity, pseudonymity, or “masking” play in shaping how we behave online, and how does that compare with more “verified” or persistent online identities?

Anonymity and pseudonymity allow people to explore different aspects of themselves, voice opinions freely, or engage in communities without the pressure of being tied to their real identities. The Social Identity Model of Deindividuation Effects (SIDE) explains that anonymity can reduce personal accountability while increasing identification with group norms, influencing how individuals act in online spaces. In contrast, verified or persistent identities, like those tied to real names or professional accounts, encourage more cautious and socially acceptable behavior due to the potential for reputational consequences. Both approaches have value: anonymity fosters creative and honest self-expression, while verified identities promote trust, continuity, and credibility in digital interactions (SAGE Journals, 2024).

References

Ferrara, E., & Yang, Z. (2017). Me, my echo chamber, and I: Introspection on social media polarization. arXiv. Retrieved from https://arxiv.org/abs/1703.04791

Frontiers in Psychology. (2021). Online identity: The construction, presentation, and perception of self in the digital space. Frontiers in Psychology. Retrieved from https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2021.696552/full

Sørensen, K. (2024). The datafied self: Digital identity and algorithmic life. Media, Culture & Society, 46(5). SAGE Journals. Retrieved from https://journals.sagepub.com/doi/full/10.1177/01634437241282240

Zhou, X., & Yang, Y. (2024). Self-discrepancy: The discrepancy between digital identity and real identity on social media and its psychological impact. ResearchGate. Retrieved from https://www.researchgate.net/publication/391771721_Self-Discrepancy_The_Discrepancy_Between_Digital_Identity_and_Real_Identity_on_Social_Media_and_Its_Psychological_Impact

#gsskittens #320critkats #sociology

Technology Labor Unrecognition

1. How is digital labor made invisible in everyday technology use?

Digital labor is often hidden behind the interfaces we interact with daily. For example, when we scroll through Instagram, it looks effortless. However, the app's functionality depends on invisible labor like content moderation, algorithm training, and data labeling, which is outsourced to underpaid workers in the Global South. This invisibility creates the illusion that technology is neutral and self-sustaining, masking the exploitation that undergirds it (Nakamura, 2015). The "covert" nature of this work mirrors how certain marginalized groups (by race, gender, or class) are historically excluded from recognition as laborers because of typical assumptions.

2. How do advertisements and marketing obscure digital labor's racial and gendered dynamics?

Tech marketing often promotes images of sleek innovation and progress, like Apple commercials that showcase creativity, individuality, and freedom. However, these ads rarely acknowledge the global labor chain that produces such products: factory workers in China under harsh conditions, or women workers in electronics assembly lines. The racial and gendered dynamics become invisible, even though they shape who is exploited and who benefits. As Daniels (2009) points out in White Supremacy in the Digital Era, digital platforms also sustain racial hierarchies secretly, even as marketing pretends to represent a universal, post-racial "future."

3. How do race and gender intersect with digital representations of labor in online media platforms?

Online spaces often reproduce stereotypes through representation. For example, in gaming, Ow's (1997) critique of the "yellowfaced cyborg" highlights how Japanese characters were reduced to violent, hypersexualized caricatures designed for white male audiences. This shows how "labor" in design and storytelling reinscribes racial and gender hierarchies. Meanwhile, women and people of color who perform digital labor as influencers or content creators often face harassment or are devalued compared to their white, male counterparts (Nakamura, 2015). This overt representation of race and gender contrasts with the covert invisibility of the workers behind the platforms.

4. What role does digital labor play in reinforcing or resisting systems like white supremacy online?

Digital labor both supports and resists systemic racism. On one hand, platforms rely on algorithms trained on biased data, which can amplify racist content. Daniels (2009) calls this "cyber racism." Content moderators, often people of color in the Global South, perform the labor of cleaning up harmful content for Western audiences, yet their work is hidden (Roberts, 2019). On the other hand, activists use digital labor to challenge white supremacy—organizing movements like #BlackLivesMatter, which make visible the injustices often obscured in mainstream media (Daniels, 2009). This shows how digital labor can reinforce systemic oppression or be mobilized as a tool for resistance.

References:

Daniels, J. (2009). Cyber racism: White supremacy online and the new attack on civil rights. Lanham: Rowman & Littlefield.

Kolko, B., Nakamura, L., & Rodman, G. (2000). Race in cyberspace. New York: Routledge.

Nakamura, L. (2015). “5 Types of Online Racism and Why You Should Care.” TEDx Talk

Ow, J. (1997). The Revenge of the Yellowfaced Cyborg Terminator.

Roberts, S. T. (2019). Behind the Screen: Content Moderation in the Shadows of Social Media. Yale University Press. #gsskittens

#gsskittens #320critkats #sociology

Memes & Culture in Storytelling

1. How do memes reflect and shape modern culture during major social or global events?

Memes act as cultural mirrors, reflecting and shaping how people process major events. During the COVID-19 pandemic, memes about lockdown life, virtual meetings, and panic-buying became shared ways to cope with uncertainty and isolation. These digital jokes were not just for laughs; they helped create a sense of global solidarity through shared experience. Similarly, during the 2020 Black Lives Matter protests, memes were powerful tools for political commentary and activism. They were used to critique systemic racism, spread educational resources, and call out performative allyship in a format that was highly accessible, as it would also go viral. Limor Shifman explains that memes have become “key instruments of public discourse” in the digital age (Shifman, 2014).

2. Why are memes considered a form of digital storytelling?

Memes function as a unique form of digital storytelling because they convey complex cultural messages quickly using humor, images, and shared references. For instance, a meme referencing a scene from Ratatouille can be reinterpreted in countless ways, ranging from commentary on imposter syndrome to critiques of control in relationships or just having simple laughs and relatability. According to Shifman (2014), memes are part of a “participatory digital culture” where users actively create and remix content that resonates with their values or communities. This means that people use memes rather than passively consuming media to express identity, form community, and contribute to cultural conversations. We see this increasingly as the younger generations adapt to these trends.

3. What role do memes play in online identity and subcultures, and what ethical concerns do they raise?

Memes often act as social markers within online subcultures, reinforcing group identity and shared language. Platforms like TikTok, Reddit, and Tumblr have their own meme trends and inside jokes that help users feel connected. A good example is the “NPC” meme, which has been used in political discourse to mock perceived conformity. However, memes also raise ethical questions. For instance, the use of African American Vernacular English (AAVE) and reaction GIFs featuring Black individuals in online memes has sparked debates around digital blackface. As Lauren Michele Jackson (2017) argues, this often results in cultural appropriation, where Black expressions are stripped of their context and used for entertainment by others, reinforcing stereotypes rather than celebrating culture.

4. How can memes reinforce or challenge social norms and power structures?

Memes can reinforce and subvert dominant social narratives, making them powerful tools for shaping public opinion. On one hand, memes often play into stereotypes or normalize certain behaviors through repetition and humor. For example, some gender-related memes perpetuate toxic masculinity or dismiss mental health struggles as jokes. On the other hand, memes can be used to challenge authority, mock political figures, or highlight inequalities. This is evident in how memes have been used to critique capitalism, poke fun at elitism, or expose racial and gender biases. As Shifman (2014) points out, memes’ remixable and shareable nature allows them to circulate rapidly, influence discourse, and even sometimes reinforce harmful ideologies, but often give voice to marginalized perspectives and enable grassroots activism.

Shifman, L. (2014). Memes in Digital Culture. MIT Press.

Jackson, L. M. (2017). “We Need to Talk About Digital Blackface in Reaction GIFs.” Teen Vogue. https://www.teenvogue.com/story/digital-blackface-reaction-gifs

#gsskittens #meme culture

Questioning Technological Hierarchies

How can facial recognition divide the use and analysis of marginalized communities?

Facial recognition technology is a great example of how bias can be built into systems. Research by Buolamwini and Gebru (2018) revealed that commercial facial recognition systems had significantly higher error rates when identifying dark-skinned individuals, especially women. These inaccuracies are not just technical flaws—they can have serious consequences. This issue acknowledges how marginalized groups often get the short end of the stick of technological advancements, mainly when those systems are used for policing and security, because they are simply not taken into mind during development.

Follow-up from question one: Who decides how to shape and create these boundaries?

Technology's design and ethical boundaries are often set by a small, homogeneous group—typically white, male, and from the Global North, like the United States. This lack of diversity can result in technologies failing to account for marginalized communities' needs and experiences. For example, voice recognition systems often struggle to understand non-standard accents or dialects (Tatman, 2017), excluding users outside the dominant linguistic norm, and specifically those in cities where English isn’t the dominant language. A more inclusive approach—such as participatory design involving the communities most affected—could lead to more ethical and equitable technologies. However, these perspectives are often sidelined in corporate tech environments, prioritizing profit and speed over inclusivity.

How do surveillance technologies unfairly target and criminalize marginalized communities?

Surveillance technologies are brought out unevenly, often targeting communities that are already over-policed and stigmatized. Predictive policing tools, for instance, use historical crime data—which already reflects racial bias—to predict future crime "hotspots," leading to a feedback loop of increased surveillance in poor and minority neighborhoods (Richardson, Schultz, & Crawford, 2019). Similarly, surveillance tools in immigration enforcement, such as biometric tracking and social media monitoring, disproportionately affect undocumented and racialized communities.

How do social media platforms' polices marginalize people of color while amplifying others?

Major platforms like Facebook, Instagram, and TikTok have also been criticized for disproportionately silencing marginalized voices. Activists—especially those advocating for racial justice, Indigenous rights, or Palestinian liberation—have reported having their content removed or accounts banned under vague or inconsistently applied guidelines (De Vynck & Zakrzewski, 2021). These algorithmic decisions are rarely transparent and often shaped by international politics or commercial interests, like more money for a company. As a result, dominant narratives are amplified, while critical voices are suppressed, reinforcing existing power dynamics.

References:

Buolamwini, J., & Gebru, T. (2018). Gender shades: Intersectional accuracy disparities in commercial gender classification. Proceedings of the 1st Conference on Fairness, Accountability and Transparency, 81, 77–91. https://proceedings.mlr.press/v81/buolamwini18a.html

De Vynck, G., & Zakrzewski, C. (2021, October 25). Facebook’s own data shows it suppresses Palestinian content. The Washington Post. https://www.washingtonpost.com/technology/2021/10/25/facebook-palestinian-suppression/

Richardson, R., Schultz, J. M., & Crawford, K. (2019). Dirty data, bad predictions: How civil rights violations impact police data, predictive policing systems, and justice. New York University Law Review, 94(1), 192–233. https://www.nyulawreview.org/issues/volume-94-number-1/dirty-data-bad-predictions-how-civil-rights-violations-impact-police-data-predictive-policing-systems-and-justice/ Tatman, R. (2017, March 21). Gender and dialect bias in YouTube’s automatic captions. Making Noise in the Machine. https://makingnoise.blog/2017/03/21/gender-and-dialect-bias-in-youtubes-automatic-captions/

#GSS320Kittens #GSSKittens #GSSCritKats

Tech Fail Leading to College Dropout

I had always been skeptical of technology advancements in place of professionals, especially in a school setting. However, to my surprise, I could handle it well because of its convenience. Seeing my grades without asking my teachers, advice on class schedules, and more. So when applying to colleges came around, the buildup of stress settled, and many of my classmates and I could rely on our computers to send our important documents to our dream universities.

Now, flash forward a couple of months, it is summer going into freshman year of university, orientation had just finished, and I had finalized my classes for the fall semester. I came to find out that there was no point in picking them. I had just been sent an email from my university stating that they had never gotten my high school transcript, something I had sent them weeks ago, and that if they don't get it within the next 3 days, they would rescind my acceptance. I would be dropped from the university.

Panic had ensued throughout my whole body. I immediately went online, re-sent my transcripts, and got a confirmation that the transcripts should reach my university within the next two business days. But just to be sure, I called the helpline at my university and tried talking to someone to ask why they hadn't gotten it in the first place, and to my surprise, no one could help me. The school had officials who handled the transcripts through email only! Even if you worked at the school yourself, you wouldn't be able to get a hold of them, and they only worked 4-5 days of the week, 8 am-4 pm.

I was stressed for the next 3 days leading up, and I'm not sure if I'd get an email confirming they had gotten my transcripts or an email stating I would be dropped from the university, which ended up being the latter. I panicked and called anyone I could from the university to help me understand why they hadn't been sent. I contacted my high school advisors to get an official transcript to deliver them myself the next day.

In the end, I wasn't the only one who had gotten dropped for the exact reason; three other girls had entered the office after me for the same reason, demanding that it be fixed, and to get our classes back. Angered parents, including my own father, were confused about how something like this could happen. The university had never admitted fault, and I never even got to see the high-up who decided she could suddenly fix "my mistake."

So, due to one minor mistake, whether it was the university's system or the website I had been told to use to send my transcripts, I was a college dropout for about a whole day before they decided to take me back.

#320CritKats

created this for a GSS course at my uni!

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