POLITICAL DISCOURSE CONTENT ANALYSIS: A CRITICAL OVERVIEW OF A COMPUTERIZED TEXT ANALYSIS PROGRAM LINGUISTIC INQUIRY AND WORD COUNT (LIWC)

By A. Yanovets, O. Smal. Naukovì zapiski Nacìonalʹnogo unìversitetu «Ostrozʹka akademìâ». Serìâ «Fìlologìâ», 2020

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The article examines and analyzes the linguistic and psychological features of political discourse using a computer-based Linguistic Inquiry and Word Count (LIWC) content analysis program to explore the relationship between political discourse and the personality of politicians. As for political discourse, it is perhaps the communicator, the linguistic personality, who plays the most important role in the communication. The linguistic personality of a politician is of particular interest in political discourse content-analysis, since it has the greatest influence on the public consciousness via mass media. Using text as a source of psychological and cognitive information has been gaining popularity. Researchers use a variety of methods to analyze texts, but Linguistic Inquiry Word Count (LIWC) has proved to be the most common technique. The analysis of linguistic patterns of political discourse shows that in the context of political speech events such as media interviews, politicians make a unique choice of lexical units, which can be interpreted as a manifestation of certain personality traits. However, despite the significance of the results, there are clear limitations to the use of computerized methodologies to make political discourse content-analysis, such as the limited interpretive capacity of software to understand pragmatic and contextual use of lexical units.

Subfields: Artificial Intelligence, Literature and Literary Theory, Social Psychology

View Paper: https://www.semanticscholar.org/paper/8f555ab3d6096fa0415924e01b058af07a64c5b8

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Citations in the corpus

Unsupervised Discovery of Implicit Gender Bias (2020, neutral): “Because the stereotype lexicons are relatively small, and scores can be dominated by a few words, we also compare LIWC scores (Pennebaker et al., 2001).”
Suicide Ideation Detection via Social and Temporal User Representations using Hyperbolic Learning (2021, methodology): “Node Degree 2,452 Median Node Degree 1.0 Node Density 1.9e−4 Power Law p(x) = Cx−α xmin 14.0 α 2.97 Table 1: Network analysis and statistics RF + TF (Sawhney et al., 2018): Feeds features such as statistical, LIWC (Pennebaker et al., 2001), n-grams, and POS counts from the tweet to a Random Forest (”
Combating Fake News (2019, background): “The third cue set was a comprehensive set of 31 cues created by including the first two cue sets along with additional Linguistic Inquiry and Word Count (LIWC) [80] based cues utilized by previous studies [10, 74], and included lexical diversity, modal verbs, passive verbs, emotiveness, exclusive te”
Characterizing the Propagation of Situational Information in Social Media During COVID-19 Epidemic: A Case Study on Weibo (2020, methodology): “Specifically, the emotional factors, perception factors, and affiliation factors are extracted using Linguistic Inquiry and Word Count (LIWC), a commonly used tool to extract the linguistic information from content [32], [33].”
CARER: Contextualized Affect Representations for Emotion Recognition (2018, background): “Some of these studies rely on affect lexicons, such as LIWC (Pennebaker et al., 2007) and WordNet Affect (Strapparava et al., 2004), to extract emotion features from a text-based corpus.”
Authorship Attribution for Neural Text Generation (2020, methodology): “Table 3 includes top-3 distinguished LIWC features among all generation methods.”
NICE: Neural Image Commenting with Empathy (2021, methodology): “With the LIWC tool, we extract a 64-dimension normalized feature vector for 2Each image can have multiple dialogue threads. each comment automatically by counting the number of words for each dictionary categories.”
Recollection versus Imagination: Exploring Human Memory and Cognition via Neural Language Models (2020, methodology): “We count psychologically relevant word categories using the Linguistic Inquiry Word Count (Pennebaker et al., 2015, LIWC;)”
Linguistic Cues to Deception and Perceived Deception in Interview Dialogues (2018, methodology): “We used LIWC (Pennebaker et al., 2001) to extract semantic features from each utterance.”
Inductive Representation Learning on Temporal Graphs (2020, background): “The user posts have textual features that are transformed into a 172-dimensional vector representing under the linguistic inquiry and word count (LIWC) categories (Pennebaker et al., 2001).”
Automatic detection of cyberbullying in social media text (2018, background): “For both languages, we included the relative frequency of all 68 psychometric categories in the Linguistic Inquiry and Word Count (LIWC) dictionary for English(Pennebaker et al., 2001) and Dutch (Zijlstra et al., 2004).”