Brendan Kennedy

Brendan Kennedy
University of Southern California | USC · Department of Computer Science

Bachelor of Science

About

38
Publications
9,164
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271
Citations
Citations since 2017
36 Research Items
271 Citations
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2017201820192020202120222023020406080100120
2017201820192020202120222023020406080100120
Introduction
I am interested in methods and theory for using language in psychological science. My current work focuses on understanding and improving the interpretability of natural language processing, integrating prior knowledge into machine learning applications, and theory-development for the psychological facets of languages and language usage.

Publications

Publications (38)
Article
Full-text available
Research has shown that accounting for moral sentiment in natural language can yield insight into a variety of on- and off-line phenomena such as message diffusion, protest dynamics, and social distancing. However, measuring moral sentiment in natural language is challenging, and the difficulty of this task is exacerbated by the limited availabilit...
Preprint
Full-text available
Language is a psychologically rich medium for human expression and communication. While it is often used in moral psychology as an intermediary between researcher and participant, much of the human experience that occurs through language — our relationships, conversations, and, in general, the everyday transmission of our thoughts — has yet to be s...
Preprint
Full-text available
The growing prominence of online hate speech is a threat to a safe and just society. This endangering phenomenon requires collaboration across the sciences in order to generate evidence-based knowledge of, and policies for, the dissemination of hatred in online spaces. To foster such collaborations, here we present the Gab Hate Corpus (GHC), consis...
Preprint
Full-text available
Existing word embedding debiasing methods require social-group-specific word pairs (e.g., "man"-"woman") for each social attribute (e.g., gender), which cannot be used to mitigate bias for other social groups, making these methods impractical or costly to incorporate understudied social groups in debiasing. We propose that the Stereotype Content Mo...
Preprint
Humans use language toward hateful ends, inciting violence and genocide, intimidating and denigrating others based on their identity. Despite efforts to better address the language of hate in the public sphere, the psychological processes underlying the development of hate remain unclear. In this work, we hypothesize that morality and hate are conc...
Article
Despite the widespread availability of COVID-19 vaccines, the United States has a depressed rate of vaccination relative to similar countries. Understanding the psychology of vaccine refusal, particularly the possible sources of variation in vaccine resistance across U.S. subpopulations, can aid in designing effective intervention strategies to inc...
Preprint
Full-text available
Moral framing and sentiment can affect a variety of online and offline behaviors, including donation, pro-environmental action, political engagement, and even participation in violent protests. Various computational methods in Natural Language Processing (NLP) have been used to detect moral sentiment from textual data, but in order to achieve bette...
Article
Full-text available
Infectious diseases have been an impending threat to the survival of individuals and groups throughout our evolutionary history. As a result, humans have developed psychological pathogen-avoidance mechanisms and groups have developed societal norms that respond to the presence of disease-causing microorganisms in the environment. In this work, we d...
Article
Full-text available
We present the Gab Hate Corpus (GHC), consisting of 27,665 posts from the social network service gab.com, each annotated for the presence of “hate-based rhetoric” by a minimum of three annotators. Posts were labeled according to a coding typology derived from a synthesis of hate speech definitions across legal precedent, previous hate speech coding...
Article
Full-text available
Online radicalization is among the most vexing challenges the world faces today. Here, we demonstrate that homogeneity in moral concerns results in increased levels of radical intentions. In Study 1, we find that in Gab—a right-wing extremist network—the degree of moral convergence within a cluster predicts the number of hate-speech messages member...
Preprint
Infectious diseases have been an impending threat to the survival of individuals and groups throughout our evolutionary history. As a result, humans have developed psychological pathogen-avoidance mechanisms and groups have developed societal norms that respond to the presence of disease-causing microorganisms in the environment. In this work, we d...
Preprint
Full-text available
Social stereotypes negatively impact individuals' judgements about different groups and may have a critical role in how people understand language directed toward minority social groups. Here, we assess the role of social stereotypes in the automated detection of hateful language by examining the relation between individual annotator biases and err...
Preprint
Despite the widespread availability of COVID-19 vaccines, the United States has a depressed rate of vaccination as of September 2021. Understanding the psychology of collective vaccine refusal, particularly the sources of variation across U.S. sub-populations, can aid in designing effective intervention strategies to increase vaccination across dif...
Preprint
Despite the widespread availability of COVID-19 vaccines, the United States has a depressed rate of vaccination as of September 2021. Understanding the psychology of collective vaccine refusal, particularly the sources of variation across U.S. sub-populations, can aid in designing effective intervention strategies to increase vaccination across dif...
Preprint
Predictive data modeling is a critical practice for the behavioral sciences; however, it is under-practiced in part due to the incorrect view that machine learning (ML) models are "black boxes," unable to be used for inferential purposes. In this work, we present an argument for the adoption of techniques from interpretable Machine Learning (ML) by...
Preprint
Full-text available
Bias mitigation approaches reduce models' dependence on sensitive features of data, such as social group tokens (SGTs), resulting in equal predictions across the sensitive features. In hate speech detection, however, equalizing model predictions may ignore important differences among targeted social groups, as hate speech can contain stereotypical...
Article
Full-text available
Understanding motivations underlying acts of hatred are essential for developing strategies to prevent such extreme behavioral expressions of prejudice (EBEPs) against marginalized groups. In this work, we investigate the motivations underlying EBEPs as a function of moral values. Specifically, we propose EBEPs may often be best understood as moral...
Article
Full-text available
Language is a psychologically rich medium for human expression and communication. While language usage has been shown to be a window into various aspects of people's social worlds, including their personality traits and everyday environment, its correspondence to people's moral concerns has yet to be considered. Here, we examine the relationship be...
Preprint
Full-text available
Due to the explosion of new sources of human language data and the rapid progression of computational methods for extracting meaning from natural language, language analysis is a promising, though complicated, category of psychological research. In this chapter, we give a modern perspective on language analysis as it applies to psychology, uniting...
Preprint
Full-text available
Online radicalization is among the most vexing challenges the world faces today. Here, we demonstrate that homogeneity in moral concerns results in increased levels of radical intentions. In Study 1, we find that in Gab – a right-wing extremist network – the degree of moral convergence within a cluster, predicts the number of hate-speech messages m...
Preprint
Full-text available
Prediction bias in machine learning models refers to unintended model behaviors that discriminate against inputs mentioning or produced by certain groups; for example, hate speech classifiers predict more false positives for neutral text mentioning specific social groups. Mitigating bias for each task or domain is inefficient, as it requires repeti...
Preprint
Full-text available
Approaches for mitigating bias in supervised models are designed to reduce models' dependence on specific sensitive features of the input data, e.g., mentioned social groups. However, in the case of hate speech detection, it is not always desirable to equalize the effects of social groups because of their essential role in distinguishing outgroup-d...
Preprint
Full-text available
Hate speech classifiers trained on imbalanced datasets struggle to determine if group identifiers like "gay" or "black" are used in offensive or prejudiced ways. Such biases manifest in false positives when these identifiers are present, due to models' inability to learn the contexts which constitute a hateful usage of identifiers. We extract post-...
Preprint
Full-text available
The growing prominence of online hate speech is a threat to a safe and just society. This endangering phenomenon requires collaboration across the sciences in order to generate evidence-based knowledge of, and policies for, the dissemination of hatred in online spaces. To foster such collaborations, here we present the Gab Hate Corpus (GHC), consis...
Preprint
Full-text available
Official reports of hate crimes in the US are under-reported relative to the actual number of such incidents. Further, despite statistical approximations, there are no official reports from a large number of US cities regarding incidents of hate. Here, we first demonstrate that event extraction and multi-instance learning, applied to a corpus of lo...
Conference Paper
Full-text available
Official reports of hate crimes in the US are under-reported relative to the actual number of such incidents. Further, despite statistical approximations , there are no official reports from a large number of US cities regarding incidents of hate. Here, we first demonstrate that event extraction and multi-instance learning, applied to a corpus of l...
Conference Paper
Full-text available
Cognitive tests used to measure individual differences are generally designed with equality in mind: the same “broadly acceptable” items are used for all participants. This has unknown consequences for equity, particularly when a single set of linguistic stimuli are used for a diverse population of language users. We hypothesized that differences i...
Preprint
Full-text available
Acts of hate have been used to silence, terrorize, and erase marginalized social groups throughout history. The rising rates of these behaviors in recent years underscores the importance of developing a better understanding of when, why, and where they occur. In this work, we present a program of research that suggests that acts of hate may often b...
Conference Paper
Full-text available
Cognitive tests have traditionally resorted to standardizing testing materials in the name of equality and because of the onerous nature of creating test items. This approach ignores participants' diverse language experiences that potentially significantly affect testing outcomes. Here, we seek to explain our prior finding of significant performanc...
Preprint
Full-text available
Research has shown that accounting for moral sentiment in natural language can yield insight into a variety of on- and off-line phenomena, such as message diffusion, protest dynamics, and social distancing. However, measuring moral sentiment in natural language is challenging and the difficulty of this task is exacerbated by the limited availabilit...
Article
Full-text available
It is widely accepted that language requires context in order to function as communication between speakers and listeners. As listeners, we make use of background knowledge — about the speaker, about entities and concepts, about previous utterances — in order to infer the speaker’s intended meaning. But even if there is consensus that these sources...
Article
Full-text available
Meaning depends on context. This applies in obvious cases like deictics or sarcasm as well as more subtle situations like framing or persuasion. One key aspect of this is the identity of the participants in an interaction. Our interpretation of an utterance shifts based on a variety of factors, including personal history, background knowledge, and...
Article
Full-text available
This paper focuses on forecasting Military Action-type events by both state and non-state actors. Here we demonstrate that the dynamics of these types of events can be adequately described by a Hidden Markov Model (HMM) where the hidden states correspond to different operational regimes of an actor, and observations correspond to event frequency—an...
Conference Paper
Computer-Aided Diagnosis (CAD) systems can provide a second opinion for either identifying suspicious regions on a medical image or predicting the degree of malignancy for a detected suspicious region. To develop a predictive model, CAD systems are trained on low-level image features extracted from image data and the class labels acquired through r...

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