Selective Outrage: Why We Care About Some Issues but Ignore Others in Society

From the recent discourse, people are opposed to the Transgender Bill and the Delimitation Bill. Protesters rallied against both bills, yet they succeeded in only one instance. Why do people protect political power, not the people’s dignity? Justice must be equal to all. Justice cannot be selective. Either it is for all, or it is not justice.  Why this selective outrage among people? The morality and values are being one-sided, not for all.  

Selective outrage prompts us to examine the prevailing morality and values, which are often one-sided. In the social media era,  people are losing morality and cultural values. Even moral values have been chosen and shaped by dominants’ perspective, personal attention and relevance, algorithmic amplification, and dominant narratives (Banet-Weiser, 2018). Is this society’s fault? Or the individual’s fault?

How injustice does not provoke equal responses. How social media projects framed justice for one side. They would care about selective things, but something can be ignored. This selective outrage can be attributed to psychological bias, emotional salience, social media influence, and social and cultural beliefs. 

The psychology behind selective Outrage

1. Cognitive Bias 

Cognitive Bias is a systematic error in thinking affecting how people process information, their perception, and decision-making (Tversky & Kahneman, 1974). These biases happen unconsciously, causing people to favour or oppose a person or ideology based on implicit and explicit factors rather than objective evidence. Misinformation is reaching the public discourse, as well as corrosive rhetorical exploitation of cognitive biases. Dual-process theory insists that cognitive biases triggered by textual and visual patterns activate specific biases among readers (Evans & Stanovich, 2013). For example, fear-based narratives in Election Campaigns affect heuristic, leading people to make decisions driven by emotion, which triggers fear, anger, and anxiety.

2. Confirmation Bias 

Confirmation bias is the tendency to seek and perceive in a way of existing beliefs and ignore those that challenge their worldview. They believe and accept a framed narrative only if they ignore other injustice practices; initially, they aren’t aware of this. The pattern seeks only existing beliefs (Nickerson, 1998).

When a news story or social media aligns with existing political or moral beliefs, it feels validating, and opposing information creates discomfort. During global conflict, social media projects stories that support their ideological side while dismissing or suffering on the other side. This selective attention reinforces group identity (Stroud, 2010).

3. Moral licensing 

People often express selective outrage about trending injustices and may feel they have contributed to and supported them, allowing themselves to ignore other injustices (Merritt, Effron, &Monin, 2010). The outrage becomes a psychological credit system that often ignores marginalised injustice and others. In recent times, moral licensing is visible in digital activism, outrage over posting and supporting Ukraine and Gaza, but not willing to address the Sudan and Myanmar crises. A user who loudly supports a trending environmental cause may feel less compelled to speak about local pollution affecting marginalised communities.

4. Emotional salience 

Emotional salience is the tendency to provoke emotions for vivid, personal, or shocking information (Kahneman, 2011). This is kind of an emotional trigger, thought perception, increase attention selectively. The emotionally unbound content has less attention. This drives the dramatic headlines visuals, which trigger immediate personal experience triggers to support selectively.

Collective Outrage can be selective 

Responsibility for selective outrage cannot be a biased individual or group. Responsibility comes from both the individual and the group to pave the way to avoid biased narratives and escape from moral values. The collective outrage emerges from trending content and amplifying partial discourse while ignoring others. The MeToo movement is collective support for women, but men’s experiences received far less attention (Banet-Weiser, 2018).

On social media, when a person posts in support of a specific group ideologically, he receives likes and comments collectively, and the followers collectively believe that. In case of anyone posting against him, it seems as negativity, people throw hatred, and the collective people are being manipulated. This reveals how collective outrage can be selective. Social media is a collective outrage cultural platform are more often exposed to one-sided discourse 

Role of social media 

Social media is becoming one of the major reasons for selective outrage. The manipulation through AI and deepfakes synthetically exploits emotional salience by projecting shocking news (Lazer et al., 20118). The algorithmic amplification and moral licensing on online platforms frame the content, making people engage, diminishing some important issues. Posting and circulating the trending content on one side.

Tools for Detection

Socia Media platform has increasing the risk of online information integrity and civic discourse. Misinformation easily spreads.  People cannot determine whether this information is factual or not. It may lead to cognitive triggers among the rational public discourse. Using these tools helps to detect and analyse the content and information. It will not precede selective outrage.  

• Media literacy tools are detecting the credibility and bias 
• Ideology tools- NewsGuard, Media Bias, Fact Check, and Ground News  
• Factuality Tools – ClaimBuster and perspective API 

Read More: Misinformation in Your Feed: When Social Media Becomes a Mental Health Risk

 1. AI Tools 

VIGIL VIrutual GuardIan angeL ( https: //github.com?aida-ugent?vigil)

 VIGIL is the First browser extension for real- time cognitive bias trigger detection and moralization detection. It has been designed for Twitter/ X- feeds and news Websites. It is also setting of auto analyze, which has a default setting of rewrite or hide. So, the user cannot see manipulative text (AIDA-Ugent, 2024).

2. Shapes AI 

Shapes AI is for identifying patterns of selective outrage. This AI tool is specially designed for cognitive bias, double standards, and hypocrisy. analyse media, politics, and social debates of cognitive bias, double standards, and hypocrisy. This tool provides concrete, real-world examples and is centred on uncovering cognitive biases that are often unnoticed in daily news consumption.

Call to Awareness 

Selective outrage is not an excuse for the overlooked injustice of others and neglect of vulnerable groups. It is a harmful inconsistency in moral values. Psychological perception, social media manipulation, and socio-cultural beliefs make selective predictions. By evaluating truth, facts, and evidence, cultivating critical thinking, and analytical skills resist cognitive bias and balance in civic discourse. Responsibility from the individual and society, and accountability from the digital platform, breaking the partial issue, speaking out for everyone.

References +

Banet-Weiser, S. (2018). Empowered: Popular feminism and popular misogyny. Duke University Press.

Evans, J. S. B. T., & Stanovich, K. E. (2013). Dual-process theories of higher cognition: Advancing the debate. Perspectives on Psychological Science, 8(3), 223–241.

Kahneman, D. (2011). Thinking, fast and slow. Farrar, Straus and Giroux.

Lazer, D. M. J., Baum, M. A., Benkler, Y., Berinsky, A. J., Greenhill, K. M., Menczer, F., … & Zittrain, J. L. (2018). The science of fake news. Science, 359(6380), 1094–1096.

Merritt, A. C., Effron, D. A., & Monin, B. (2010). Moral self-licensing: When being good frees us to be bad. Social and Personality Psychology Compass, 4(5), 344–357.

Nickerson, R. S. (1998). Confirmation bias: A ubiquitous phenomenon in many guises. Review of General Psychology, 2(2), 175–220.

Stroud, N. J. (2010). Polarisation and partisan selective exposure. Journal of Communication, 60(3), 556–576.

Tversky, A., & Kahneman, D. (1974). Judgment under uncertainty: Heuristics and biases. Science, 185(4157), 1124–1131.

AIDA-Ugent. (2024). VIGIL: Virtual Guardian Angel. GitHub repository.