Potential Bias
Overview
Rather than focusing solely on detecting bias in the content itself—as is common in traditional media analysis—this section emphasizes the psychological biases that shape how audiences interpret media online. As viewers, we may have limited control over the social or political leanings embedded in the content we consume, but we can strengthen our own media literacy. By learning to recognize the subconscious triggers and heuristic shortcuts we’re naturally prone to, we give ourselves the tools to engage with digital content more critically and responsibly.
For any media or topical content, our analysis primarily highlights the psychological biases that creators may leverage to influence perception and achieve their intended outcomes
Justification and Research
While far from encapsulating all existing potential biases which people may come across online, the eight biases which we have chosen, when taken in unison, encompass a wide range of psychological processes: memory and retrieval (avaliibility, recency), emotional influence (affect heurisitc), social dynamics (bandwagon, in-group), and judgement under uncertainty (anchoring, confirmation). Furthermore, these biases have backing in foundational research in cognitive and social science. Below we have listed a respective study that investigates each of the 8 biases we labeled.
1. Confirmation Bias: Nickerson, R. S. (1998). Confirmation bias: A ubiquitous phenomenon in many guises. This review outlines how people tend to favor information that confirms their preexisting beliefs, particularly relevant in echo chambers and algorithm-driven feeds.
2. Anchoring Bias: Tversky, A., & Kahneman, D. (1974). Judgment under Uncertainty: Heuristics and Biases. This foundational work showed how initial information (“anchors”) significantly influences later judgments, even when irrelevant.
3. Availability Heuristic: Combs, B., & Slovic, P. (1979). Newspaper coverage of causes of death. This study demonstrated how media exposure makes certain risks feel more common or probable than they really are.
4. Recency Bias: Miller, J. M., & Krosnick, J. A. (2000). News media impact on the ingredients of presidential evaluations: Politically knowledgeable citizens are guided by a trusted source. Their research shows people overweight recent information when forming opinions, a key trait of recency bias.
5. Affect Heuristic: Slovic, P. et al. (2004). Risk as analysis and risk as feelings. This study explains how emotional reactions often substitute for analytical reasoning in decision-making.
6. Bandwagon Effect: Sundar, S. S. (2008). The MAIN model: A heuristic approach to understanding technology effects on credibility. Shows how popularity signals like shares and likes influence perceived credibility and drive further engagement.
7. Authority Bias: Milgram, S. (1963). Behavioral study of obedience. This iconic experiment revealed the extent to which individuals obey authority figures—even when doing so goes against their personal morals.
8. In-group Bias: Tajfel, H., & Turner, J. C. (1979). An integrative theory of intergroup conflict. This study introduced Social Identity Theory, demonstrating how people favor members of their own group, especially under polarized or tribal online dynamics.