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Analyzing Bias with Text, Images, And Sound

Paper Session

Saturday, Jan. 8, 2022 3:45 PM - 5:45 PM (EST)

Hosted By: American Economic Association
  • Chair: Carlo Schwarz, Bocconi University

Visual Representation and Stereotypes in News Media

Elliott Ash
ETH Zurich
Ruben Durante
Pompeu Fabra University
Carlo Schwarz
Bocconi University


We propose a new method for measuring gender and ethnic stereotypes in news reports. By combining computer vision and natural language processing tools, the method allows us to analyze both images and text as well as the interaction between the two. We apply this approach to over 2 million web articles published in the New York Times and Fox News between 2000 and 2020. We find that in both outlets, men and whites are generally over-represented relative to their population share, while women and Hispanics are under-represented. We also document that news content perpetuates common stereotypes such as associating Blacks and Hispanics with low-skill jobs, crime, and poverty, and Asians with high-skill jobs and science. For jobs, we show that the relationship between visual representation and racial stereotypes holds even after controlling for the actual share of a group in a given occupation. Finally, we find that group representation in the news is influenced by the gender and ethnic identity of authors and editors.

Emotions and Violence: The Effect of Biased News on Hate Crimes

Jacob Miller
University of Zurich


Can what the media discusses, and the way it discusses it, have consequences for hateful behavior? To examine this question, I measure the effect of biased news about immigration on violent crimes towards immigrants. Motivated by a recent literature establishing that unexpected sports losses can stimulate emotional reactions, I utilize unexpected sports losses as a trigger for emotionally driven violence. When the local National Hockey League (NHL) team unexpectedly loses, I observe heightened hate crimes against immigrants if cable news had been intensely discussing immigration. To dig deeper into the intensive margin of this effect, I develop and utilize state-of-the-art machine learning algorithms that can quantify immigration bias in two different ways: from the text of the broadcasts, the degree of slant, and from the audio, the degree of anger. I find that the effects are further magnified when the media's coverage is especially angry, and in the case of Fox News, if the coverage is slanted against immigrants. These novel findings establish that, in the context of emotionally driven violence, the topics and tone of the news cycle can play a consequential role.

The (great) Persuasion Divide? Gender Disparities in Debate Speeches & Evaluations

Huyen Nguyen
University of Hamburg


Do men and women persuade differently? Are they evaluated differently? Using a novel data set of 1517 speech transcripts, evaluation scores and demographic data from highest-profile intervarsity debate tournaments, this research investigates spoken verbal tactics across genders and any ensuing impacts on their performance evaluations. The well-defined competitive rules of debate tournaments along with transparent merit-based evaluation system enables me to disentangle whether gender disparities in outcomes is due to differences in speech behavior or gender-specific evaluation standards (i.e. discrimination). To extract persuasion-relevant linguistic variables, I use widely validated word-and-phrase-based methods from persuasiveness studies. This method provides easily replicable linguistic variables to analyze unstructured, high-dimensional impromptu spoken text data, with limited observations. I find significant variation in speech patterns of male and female speakers. Specifically, female speakers use more personal and disclosing speaking styles, with more hedging phrases and disfluencies in their speeches. In their answers to questions from opponents, they negate less while having notably longer and more vague answers. Evaluation-wise, within debates, except for disfluencies, there is no robust evidence of gender-specific evaluation standards. These findings suggest that women receive lower scores than men because their speeches contain more score-reducing and fewer score-enhancing features, rather than discrimination.

Physician Practice Style for Mental Health Conditions: The Case of ADHD

Kelli Marquardt
Federal Reserve Bank of Chicago


While there is a robust literature documenting the importance of physician practice style variation in physical health applications, little is known about the role of physicians in explaining patient mental health outcomes and associated expenditures. This paper uses novel data on doctor note text together with natural language processing techniques to estimate and document heterogeneity in physician practice style for diagnosing Attention Deficit Hyperactivity Disorder (ADHD). I find significant variation in both diagnostic intensity (the mean propensity to diagnose) and diagnostic compliance (the weight that physicians place on medical guidelines). Physician characteristics can explain some of this heterogeneity, with both females and recent graduates having higher diagnostic compliance and lower diagnostic intensity than their respective counterparts. Since mental health diagnostic errors lead to excess medical and societal spending, the findings in this paper encourage a re-evaluation of mental health identification, though perhaps targeted at specific sub-groups of physicians.

Ashesh Rambachan
Harvard University
Oda Nedregard
BI Norwegian Business School
JEL Classifications
  • J7 - Labor Discrimination
  • L8 - Industry Studies: Services