The Psychological Meaning of Words: LIWC and Computerized Text Analysis Methods

By Yla R. Tausczik, James W. Pennebaker. 2010

5,387 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Social Psychology, Human-Computer Interaction

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Citations in the corpus

Lawyers are Dishonest? Quantifying Representational Harms in Commonsense Knowledge Resources (2021, background): “collect a list of biased words that could represent favoritism and prejudice from LIWC (Tausczik and Pennebaker, 2010) and Empath (Fast et al.”
Connotation Frames of Power and Agency in Modern Films (2017, methodology): “LIWC re-sults that are not discussed below can be found in the appendix (Tables 4 and 5).”
Modeling Naive Psychology of Characters in Simple Commonsense Stories (2018, background): “Ghosh et al. (2017) modulate language model distributions by increasing the probability of words that express certain affective LIWC (Tausczik and Pennebaker, 2016) categories.”
Emotion shapes the diffusion of moralized content in social networks (2017, methodology): “What factors inform our intuitions about morality? Influential theories in psychology maintain that our moral sense is shaped by the social world (1), insofar as decades of research demonstrate that social communities influence moral development in children (2) and account for cross-cultural variati”
Human heuristics for AI-generated language are flawed (2022, methodology): “We developed an initial set of language features motivated by participants’ explanations of their judgments, to which we added various statistical metrics and commonly analyzed psychological language features such as LIWC (21).”
Judgment of the Humanness of an Interlocutor Is in the Eye of the Beholder (2011, background): “LIWC’ validity of measure has been demonstrated for emotional expression presented in text [21] and for detecting attention focus, thinking style, emotionality, social relationships, and individual differences [22].”
Data ex Machina: Introduction to Big Data (2017, background): “The Linguistic Inquiry and Word Count dictionary is perhaps the best known in the social sciences (Tausczik & Pennebaker 2010).”
A Social Media Study on the Effects of Psychiatric Medication Use (2019, methodology): “The 3rd set consists of normalized use of psycholinguistic attributes in the posts, i.e, distribution across 50 categories in the LIWC lexicon (Tausczik and Pennebaker 2010), across affective, cognitive, lexical, stylistic, and social attributes.”
Synchronous and Asynchronous Discussions: Effects on Cooperation, Belonging, and Affect (2018, methodology): “Emotion words have been used to measure a person’s immersion in an experience or event (Tausczik & Pennebaker, 2010).”
Framing Climate Change: Economics, Ideology, and Uncertainty in American News Media Content From 1988 to 2014 (2019, methodology): “Secondly, capturing simpler components of language, such as risk and tense, is much more straightforward with dictionary methods, especially with a pre-assembled and verified dictionaries included in Linguistic Inquiry and Word Count (LIWC) software (Tausczik and Pennebaker, 2010).”
Parsing organizational culture: How the norm for adaptability influences the relationship between culture consensus and financial performance in high-technology firms (2014, methodology): “K report using the Linguistic Inquiry and Word Count (LIWC) approach (Tausczik & Pennebaker, 2010).”
Evaluating content quality and helpfulness of online product reviews: The interplay of review helpfulness vs. review content (2012, background): “Recent developments in computational linguistics related algorithms that asses this further, such as the Linguistic Inquiry and Word Count (Tausczik and Pennebaker 2010), can also assist in researching this aspect n additional depth.”
Approaches, Tools and Applications for Sentiment Analysis Implementation (2015, methodology): “Another method is the Linguistic Inquiry and Word Count [37] that allows analysing not only positive and negative but also emotional, cognitive, and structural components of a text based on the use of a dictionary containing words and their classified categories.”
Teacher vision: expert and novice teachers’ perception of problematic classroom management scenes (2016, methodology): “In our analysis, all of the semantic primes represent regular verbs defining ‘‘primary categories and actions dictated by the speaker’’ (Tausczik and Pennebaker 2010, p. 345).”