Performing Multilingual Analysis With Linguistic Inquiry and Word Count 2015 (LIWC2015). An Equivalence Study of Four Languages

Dudău, Diana Paula; Sava, Florin Alin

doi:10.3389/fpsyg.2021.570568

Cited by 19 publications

(21 citation statements)

References 43 publications

(69 reference statements)

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“…According to the study, recent research gathered tweets from people who had reportedly been diagnosed with depression. Linguistic Inquiry and Word Count (LIWC) was a tool used to analyze and track changes in social interaction on Twitter using the collected messages [ 3 ]. To better understand the risk of postpartum depression among Facebook users, another study used specialized psychometric measures to compare the levels of postpartum depression between the pre-and postnatal periods.…”

Section: Introductionmentioning

confidence: 99%

Detection of Types of Mental Illness through the Social Network Using Ensembled Deep Learning Model

Nasrullah

Jalali

2022

Computational Intelligence and Neuroscience

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In today’s era, social networking platforms are widely used to share emotions. These types of emotions are often analyzed to predict the user’s behavior. In this paper, these types of sentiments are classified to predict the mental illness of the user using the ensembled deep learning model. The Reddit social networking platform is used for the analysis, and the ensembling deep learning model is implemented through convolutional neural network and the recurrent neural network. In this work, multiclass classification is performed for predicting mental illness such as anxiety vs. nonanxiety, bipolar vs. nonbipolar, dementia vs. nondementia, and psychotic vs. nonpsychotic. The performance parameters used for evaluating the models are accuracy, precision, recall, and F1 score. The proposed ensemble model used for performing the multiclass classification has performed better than the other models, with an accuracy greater than 92% in predicting the class.

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Section: Introductionmentioning

confidence: 99%

Detection of Types of Mental Illness through the Social Network Using Ensembled Deep Learning Model

Nasrullah

Jalali

2022

Computational Intelligence and Neuroscience

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“…To avoid the lack of equivalence between results of analyses in different languages, it is possible to perform within-language standardization, i.e., use the mean and standard deviation of the third person singular variable as the reference parameters for rescaling the values. As the authors state, when comparing the four LIWC language adaptations (English, Dutch, Brazilian Portuguese, and Romanian), the unadjusted calculations show little sign of cross-language equivalence compared to the situation where language specificities are considered, that is, via within-language standardization (Dudãu and Sava, 2021).…”

Section: Dealing With Cross-language Adaptation Of Methodsmentioning

confidence: 96%

“…The large number of existing studies speaks to the high relevance of this research, both in terms of establishing consensus between studies and in revealing relationships with other variables as support for concurrent validity with the results of established measures. However, recent studies have also raised important questions about the generalizability of existing findings beyond the original context of investigation, which has highlighted potential constraints on their validity in different languages and cultures (Garimella et al, 2016;Basnight-Brown and Altarriba, 2018;Jackson et al, 2019;Sánchez-Rada and Iglesias, 2019;Chen et al, 2020;Thompson et al, 2020;Dudãu and Sava, 2021). The results of the studies also indicate that the comparison and psychological interpretation of linguistic phenomena between different cultures and languages is subject to several fundamental challenges.…”

Section: Challenges In Cross-language Psychological Text Analysismentioning

confidence: 99%

“…Many adjustments were also made in the Romanian adaptation, for example in verb tense, grammatical gender, or diacritics processing (Dudãu and Sava, 2020). To sum up, every translation of the LIWC dictionary involves many decisions about which entries (words or categories) should be kept, dropped, or added, and each decision is necessarily a tradeoff between computational feasibility and linguistic accuracy (Dudãu and Sava, 2021).…”

Section: Analysis Of Non-semantic Levels Of Languagementioning

confidence: 99%

“…Another issue concerns the specificity of text samples on which validity and equivalence tests were performed. In this sense, the communication context (text type, genre, register) is an important factor that can produce substantial variation both in the frequency of language features and in the associations with other variables, especially psychological ones (Pennebaker et al, 2007;Daems et al, 2013;Haider and Palmer, 2017;Biber and Conrad, 2019;Kučera et al, 2020;Dudãu and Sava, 2021). Differences in the sensitivity of LIWC for detecting psychological markers in different types of text (English only), were shown in the meta-analysis of Chen et al (2020), in which, for example, the strength of the relationship between extraversion and positive emotion words varied significantly and substantially across communication contexts (e.g., asynchronous/synchronous and public/private communication).…”

Section: Cross-language Evaluation Of Linguistic Inquiry and Word Countmentioning

confidence: 99%

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Beyond English: Considering Language and Culture in Psychological Text Analysis

Kučera

Mehl

2022

Front. Psychol.

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The paper discusses the role of language and culture in the context of quantitative text analysis in psychological research. It reviews current automatic text analysis methods and approaches from the perspective of the unique challenges that can arise when going beyond the default English language. Special attention is paid to closed-vocabulary approaches and related methods (and Linguistic Inquiry and Word Count in particular), both from the perspective of cross-cultural research where the analytic process inherently consists of comparing phenomena across cultures and languages and the perspective of generalizability beyond the language and the cultural focus of the original investigation. We highlight the need for a more universal and flexible theoretical and methodological grounding of current research, which includes the linguistic, cultural, and situational specifics of communication, and we provide suggestions for procedures that can be implemented in future studies and facilitate psychological text analysis across languages and cultures.

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Large Language Model–Based Responses to Patients’ In-Basket Messages

Small,

Wiesenfeld,

Brandfield-Harvey

et al. 2024

JAMA Netw Open

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ImportanceVirtual patient-physician communications have increased since 2020 and negatively impacted primary care physician (PCP) well-being. Generative artificial intelligence (GenAI) drafts of patient messages could potentially reduce health care professional (HCP) workload and improve communication quality, but only if the drafts are considered useful.ObjectivesTo assess PCPs’ perceptions of GenAI drafts and to examine linguistic characteristics associated with equity and perceived empathy.Design, Setting, and ParticipantsThis cross-sectional quality improvement study tested the hypothesis that PCPs’ ratings of GenAI drafts (created using the electronic health record [EHR] standard prompts) would be equivalent to HCP-generated responses on 3 dimensions. The study was conducted at NYU Langone Health using private patient-HCP communications at 3 internal medicine practices piloting GenAI.ExposuresRandomly assigned patient messages coupled with either an HCP message or the draft GenAI response.Main Outcomes and MeasuresPCPs rated responses’ information content quality (eg, relevance), using a Likert scale, communication quality (eg, verbosity), using a Likert scale, and whether they would use the draft or start anew (usable vs unusable). Branching logic further probed for empathy, personalization, and professionalism of responses. Computational linguistics methods assessed content differences in HCP vs GenAI responses, focusing on equity and empathy.ResultsA total of 16 PCPs (8 [50.0%] female) reviewed 344 messages (175 GenAI drafted; 169 HCP drafted). Both GenAI and HCP responses were rated favorably. GenAI responses were rated higher for communication style than HCP responses (mean [SD], 3.70 [1.15] vs 3.38 [1.20]; P = .01, U = 12 568.5) but were similar to HCPs on information content (mean [SD], 3.53 [1.26] vs 3.41 [1.27]; P = .37; U = 13 981.0) and usable draft proportion (mean [SD], 0.69 [0.48] vs 0.65 [0.47], P = .49, t = −0.6842). Usable GenAI responses were considered more empathetic than usable HCP responses (32 of 86 [37.2%] vs 13 of 79 [16.5%]; difference, 125.5%), possibly attributable to more subjective (mean [SD], 0.54 [0.16] vs 0.31 [0.23]; P &lt; .001; difference, 74.2%) and positive (mean [SD] polarity, 0.21 [0.14] vs 0.13 [0.25]; P = .02; difference, 61.5%) language; they were also numerically longer (mean [SD] word count, 90.5 [32.0] vs 65.4 [62.6]; difference, 38.4%), but the difference was not statistically significant (P = .07) and more linguistically complex (mean [SD] score, 125.2 [47.8] vs 95.4 [58.8]; P = .002; difference, 31.2%).ConclusionsIn this cross-sectional study of PCP perceptions of an EHR-integrated GenAI chatbot, GenAI was found to communicate information better and with more empathy than HCPs, highlighting its potential to enhance patient-HCP communication. However, GenAI drafts were less readable than HCPs’, a significant concern for patients with low health or English literacy.

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Performing Multilingual Analysis With Linguistic Inquiry and Word Count 2015 (LIWC2015). An Equivalence Study of Four Languages

Cited by 19 publications

References 43 publications

Detection of Types of Mental Illness through the Social Network Using Ensembled Deep Learning Model

Detection of Types of Mental Illness through the Social Network Using Ensembled Deep Learning Model

Beyond English: Considering Language and Culture in Psychological Text Analysis

Large Language Model–Based Responses to Patients’ In-Basket Messages

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