Using natural language processing to understand people and culture.

Berger, Jonah; Packard, Grant

doi:10.1037/amp0000882

Cited by 57 publications

(51 citation statements)

References 95 publications

(125 reference statements)

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“…Finally, these findings highlight the value of Natural Language Processing and emerging computational approaches to study human behavior (see Berger & Packard, 2022 for a recent review). Language in an integral part of everyday life, and a wealth of related data from social media posts and online reviews to song lyrics, and movie scripts is now available.…”

Section: Discussionmentioning

confidence: 85%

The speed of stories: Semantic progression and narrative success.

Santos¹,

Berger²

2022

Journal of Experimental Psychology: General

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Why are some narratives more successful? Although this question has ancient roots, studying it empirically has been challenging. We suggest that semantic progression (i.e., semantic similarity between adjoining portions of a narrative) might shape audience responses but that this role changes over the course of a narrative. Specifically, although slower semantic progression (i.e., greater semantic similarity between adjoining portions) is beneficial at the beginning of narratives, faster semantic progression is beneficial toward the end. To test this possibility, we used natural language processing and machine learning to analyze over 40,000 movie scripts. Consistent with our theorizing, deep-learning-based embeddings find that movies with slower semantic progression early and faster semantic progression later are evaluated more positively. Analysis of over 10,000 TV episodes finds similar results. Overall, these findings shed light on what makes narratives engaging, deepen understanding of what drives cultural success, and underscore the value of emerging computational approaches to understand human behavior.

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Section: Discussionmentioning

confidence: 85%

The speed of stories: Semantic progression and narrative success.

Santos¹,

Berger²

2022

Journal of Experimental Psychology: General

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“…The most common approach for automated textual analysis, particularly among consumer psychologists, is dictionaries (for recent reviews see Berger et al, 2020; Berger & Packard, 2022; Humphreys & Wang, 2018). Dictionaries usually involve a mapping between words or phrases and key psychological constructs.…”

Section: Analyzing Languagementioning

confidence: 99%

Wisdom from words: The psychology of consumer language

Berger

Packard

2022

Consumer Psychology Review

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Language plays a fundamental role in every aspect of life. But only recently has research begun to understand the role of language in consumer behavior. This paper offers an integrative discussion of research on the language of consumer psychology.We review some of the main areas of inquiry and discuss some key methodological approaches (e.g., automated textual analysis) that have been crucial to the area's development. Further, we outline some broad issues and opportunities in the space and highlight potential directions for future research. We hope to encourage more consumer psychologists to consider the great potential in producing new conceptual

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“…It is therefore no surprise that researchers have begun automating the process of using psycholinguistic cues to make inferences regarding the emotional states, personality traits, values, and needs of users [46,47,23,48,24]. Dictionary-based approaches, such as LIWC and NRC lexicon, provide a predetermined list of common psycholinguistic cues that have been developed, trained, and validated through multiple surveys and labeling tasks with human coders.…”

Section: Psycholinguistic Cuesmentioning

confidence: 99%

Who will share Fake-News on Twitter? Psycholinguistic cues in online post histories discriminate Between actors in the misinformation ecosystem

Schoenmueller¹,

Blanchard²,

Johar³

2022

Preprint

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The spread of misinformation or fake-news is a global concern that undermines progress on issues such as protecting democracy and public health. Past research aiming to combat its spread has largely focused on identifying its semantic content and media outlets publishing such news. In contrast, we aim to identify individuals who are more likely to share fake-news by studying the language of actors in the fake-news ecosystem (such as fake-news sharers, fact-check sharers and random twitter users), and creating a linguistic profile of them. Fake-news sharers and fact-check sharers use significantly more high-arousal negative emotions in their language, but fake-news sharers express more existentially-based needs than other actors. Incorporating psycholinguistic cues as inferred from their tweets into a model of socio-demographic predictors considerably improves classification accuracy of fake-news sharers. The finding that fake-news sharers differ in important ways from other actors in the fake-news ecosystem (such as in their existential needs), but are also similar to them in other ways (such as in their anger levels), highlights the importance of studying the entire fake-news ecosystem to increase accuracy in identification and prediction. Our approach can help mitigate fake-news sharing by enabling platforms to pre-emptively screen potential fake-news sharers' posts.Keywords fake-news • fact-check • social media • emotions • personality • text-mining * Citation: Schoenmueller, Blanchard, and Johar. Who will share Fake-News on Twitter? Psycholinguistic cues in online post histories discriminate Between actors in the misinformation ecosystem, working paper.

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Using natural language processing to understand people and culture.

Cited by 57 publications

References 95 publications

The speed of stories: Semantic progression and narrative success.

The speed of stories: Semantic progression and narrative success.

Wisdom from words: The psychology of consumer language

Who will share Fake-News on Twitter? Psycholinguistic cues in online post histories discriminate Between actors in the misinformation ecosystem

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