scholarly journals Using social media to explore the linguistic features in female adults with childhood sexual abuse by Linguistic Inquiry and Word Count

2019 ◽  
Vol 1 (3) ◽  
pp. 181-189 ◽  
Author(s):  
Wenyu Wan ◽  
Jiumo Sun ◽  
Jiehan Liu ◽  
Shu‐Wen Yang ◽  
Mingming Liu ◽  
...  
Keyword(s):  
Social Media ◽  
Sexual Abuse ◽  
Childhood Sexual Abuse ◽  
Word Count ◽  
Linguistic Features ◽  
Linguistic Inquiry

scholarly journals Emotion Language in Trauma Narratives Is Associated With Better Psychological Adjustment Among Survivors of Childhood Sexual Abuse

2017 ◽  
Vol 36 (6) ◽  
pp. 628-653 ◽  
Author(s):  
Britney M. Wardecker ◽  
Robin S. Edelstein ◽  
Jodi A. Quas ◽  
Ingrid M. Cordón ◽  
Gail S. Goodman
Keyword(s):  
Mental Health ◽  
Sexual Abuse ◽  
Childhood Sexual Abuse ◽  
Negative Emotion ◽  
Trauma Narratives ◽  
Abuse Severity ◽  
Linguistic Inquiry ◽  
Emotional Effects ◽  
Emotion Language

Traumatized individuals are often encouraged to confront their experiences by talking or writing about them. However, survivors of childhood sexual abuse (CSA) might find it especially difficult to process abuse experiences, particularly when the abuse is more severe. The current study examined whether CSA survivors who use emotion language when describing their abuse experiences exhibit better mental health. We analyzed the trauma narratives of 55 adults who, as children, were part of a larger study of the long-term emotional effects of criminal prosecutions on CSA survivors. Abuse narratives were analyzed using the Linguistic Inquiry and Word Count program. We examined whether positive and negative emotion language in participants’ abuse narratives were associated with self- and caregiver-reported mental health symptoms and whether these associations differed by abuse severity. As hypothesized, participants who used more positive and negative emotion language had better psychological outcomes, especially when the abuse was more severe.

Suicidal Traits in Marilyn Monroe’s Fragments

Crisis ◽  
2013 ◽  
Vol 34 (2) ◽  
pp. 124-130 ◽  
Author(s):  
M. Fernández-Cabana ◽  
A. García-Caballero ◽  
M. T. Alves-Pérez ◽  
M. J. García-García ◽  
R. Mateos
Keyword(s):  
Language Use ◽  
Negative Emotion ◽  
Rank Correlation ◽  
First Person ◽  
Word Count ◽  
Linguistic Features ◽  
Emotion Words ◽  
Spearman’S Rank Correlation ◽  
Linguistic Inquiry ◽  
Linguistic Categories

Background: Linguistic inquiry and word count (LIWC), a computerized method for text analysis, is often used to examine suicide writings in order to characterize the quantitative linguistic features of suicidal texts. Aims: To analyze texts compiled in Marilyn Monroe’s Fragments using LIWC, in order to explore the use of different linguistic categories in her narrative over the years. Method: Selected texts were grouped into four periods of similar word count and processed with LIWC. Spearman’s rank correlation was used to assess changes in language use across the documents over time. The Kruskal-Wallis test was applied to compare means between periods and for each of the 80 LIWC output scores. Results: Significant differences (p < .05) were found in 11 categories, the most relevant being a progressive decrease in the use of negative emotion words, a reduction in the use of long words in the third period, and an increase in the proportion of personal pronouns used as Monroe approached the time of her death. Conclusions: The consistently elevated usage of first-person personal singular pronouns and the consistently diminished usage of first-person personal plural pronouns are in line with previous studies linking this pattern with a low level of social integration, which has been related to suicide according to different theories.

scholarly journals Predicting Cardiovascular Risk Using Social Media Data: Performance Evaluation of Machine-Learning Models

JMIR Cardio ◽  
10.2196/24473 ◽  
2021 ◽  
Vol 5 (1) ◽  
pp. e24473
Author(s):  
Anietie U Andy ◽  
Sharath C Guntuku ◽  
Srinath Adusumalli ◽  
David A Asch ◽  
Peter W Groeneveld ◽  
...  
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Learning Model ◽  
Discriminatory Power ◽  
Risk Scores ◽  
Atherosclerotic Cardiovascular Disease ◽  
Word Count ◽  
Machine Learning Model ◽  
Linguistic Inquiry ◽  
Ascvd Risk

Background Current atherosclerotic cardiovascular disease (ASCVD) predictive models have limitations; thus, efforts are underway to improve the discriminatory power of ASCVD models. Objective We sought to evaluate the discriminatory power of social media posts to predict the 10-year risk for ASCVD as compared to that of pooled cohort risk equations (PCEs). Methods We consented patients receiving care in an urban academic emergency department to share access to their Facebook posts and electronic medical records (EMRs). We retrieved Facebook status updates up to 5 years prior to study enrollment for all consenting patients. We identified patients (N=181) without a prior history of coronary heart disease, an ASCVD score in their EMR, and more than 200 words in their Facebook posts. Using Facebook posts from these patients, we applied a machine-learning model to predict 10-year ASCVD risk scores. Using a machine-learning model and a psycholinguistic dictionary, Linguistic Inquiry and Word Count, we evaluated if language from posts alone could predict differences in risk scores and the association of certain words with risk categories, respectively. Results The machine-learning model predicted the 10-year ASCVD risk scores for the categories <5%, 5%-7.4%, 7.5%-9.9%, and ≥10% with area under the curve (AUC) values of 0.78, 0.57, 0.72, and 0.61, respectively. The machine-learning model distinguished between low risk (<10%) and high risk (>10%) with an AUC of 0.69. Additionally, the machine-learning model predicted the ASCVD risk score with Pearson r=0.26. Using Linguistic Inquiry and Word Count, patients with higher ASCVD scores were more likely to use words associated with sadness (r=0.32). Conclusions Language used on social media can provide insights about an individual’s ASCVD risk and inform approaches to risk modification.

scholarly journals Changes in Language Style and Topics in an Online Eating Disorder Community at the Beginning of the COVID-19 Pandemic: Observational Study (Preprint)

2021 ◽  
Author(s):  
Johannes Feldhege ◽  
Markus Moessner ◽  
Markus Wolf ◽  
Stephanie Bauer
Keyword(s):  
Mental Health ◽  
Social Media ◽  
Cognitive Processing ◽  
Social Life ◽  
Topic Model ◽  
Support Networks ◽  
Word Count ◽  
Recovery Orientation ◽  
Linguistic Inquiry ◽  
Mean Differences

BACKGROUND COVID-19 has affected individuals with lived experience of eating disorders (EDs), with many reporting higher psychological distress, higher prevalence of ED symptoms, and compensatory behaviors. The COVID-19 pandemic and the health and safety measures taken to contain its spread also disrupted routines and reduced access to familiar coping mechanisms, social support networks, and health care services. Social media and the ED communities on social media platforms have been an important source of support for individuals with EDs in the past. So far, it is unknown how discussions in online ED communities changed as offline support networks were disrupted and people spent more time at home in the first months of the COVID-19 pandemic. OBJECTIVE The aim of this study is to identify changes in language content and style in an online ED community during the initial onset of the COVID-19 pandemic. METHODS We extracted posts and their comments from the ED community on the social media website Reddit and concatenated them to comment threads. To analyze these threads, we applied top-down and bottom-up language analysis methods based on topic modeling with latent Dirichlet allocation and 13 indicators from the Linguistic Inquiry and Word Count program, respectively. Threads were split into prepandemic (before March 11, 2020) and midpandemic (after March 11, 2020) groups. Standardized mean differences were calculated to estimate change between pre- and midpandemic threads. RESULTS A total of 17,715 threads (n=8772, 49.5% prepandemic threads; n=8943, 50.5% midpandemic threads) were extracted from the ED community and analyzed. The final topic model contained 21 topics. CIs excluding zero were found for standardized mean differences of 15 topics and 9 Linguistic Inquiry and Word Count categories covering themes such as ED symptoms, mental health, treatment for EDs, cognitive processing, social life, and emotions. CONCLUSIONS Although we observed a reduction in discussions about ED symptoms, an increase in mental health and treatment-related topics was observed at the same time. This points to a change in the focus of the ED community from promoting potentially harmful weight loss methods to bringing attention to mental health and treatments for EDs. These results together with heightened cognitive processing, increased social references, and reduced inhibition of negative emotions detected in discussions indicate a shift in the ED community toward a pro-recovery orientation.

scholarly journals Language of Criminal Confessions

2021 ◽  
Vol 2 (3) ◽  
pp. 205-225
Author(s):  
Lucrezia Rizzelli ◽  
Saul Kassin ◽  
Tammy Gales
Keyword(s):  
High Frequency ◽  
National Registry ◽  
False Confessions ◽  
Word Count ◽  
Personal Pronouns ◽  
Linguistic Features ◽  
Model Sample ◽  
Linguistic Inquiry ◽  
Innocence Project ◽  
Impersonal Pronouns

Confession evidence is powerfully persuasive, and yet many wrongful convictions involving false confessions have surfaced in recent years (Innocence Project, 2021; National Registry of Exonerations, 2021). Although police are trained to corroborate admissions of guilt, research shows that most false confessions contain accurate details and other content cues suggesting credibility as well as extrinsic evidence of guilt. Hence, a method is needed to help distinguish true and false confessions. In this study, we utilized a corpus-based approach to outline the linguistic features of two sets of confessions: those that are presumed true (n = 98) and those that have been proven false (n = 37). After analyzing the two corpora in LIWC (Linguistic Inquiry and Word Count) to identify significant categories, we created a logistic regression model that distinguished the two corpora based on three identified predictors: personal pronouns, impersonal pronouns, and conjunctions. In a first sample comprised of 25 statements per set, the model correctly categorized 37 out of 50 confessions (74%); in a second out-of-model sample, the predictors accurately classified 20 of 24 confessions (83.3%). A high frequency of impersonal pronouns was associated with confessions proven false, while a high frequency of conjunctions and personal pronouns were associated with confessions presumed to be true. Several patterns were observed in the corpora. In the latter set of confessions, for example, “I” was often followed by a lexical verb, a pattern less frequent in false confessions. Although these data are preliminary and not to be used for practical diagnostic purposes, the findings suggest that additional research is warranted.

Developing and validating the A-B-C framework of information diffusion on social media

2016 ◽  
Vol 20 (1) ◽  
pp. 272-292 ◽  
Author(s):  
Yuhua (Jake) Liang ◽  
Kerk F Kee
Keyword(s):  
Social Media ◽  
Information Diffusion ◽  
Instructional Material ◽  
Original Research ◽  
Word Count ◽  
Operational Strategies ◽  
Social Media Platforms ◽  
Linguistic Inquiry ◽  
Communication Material ◽  
Blog Posts

This research addresses the problem of promoting information diffusion, the extent to which information spreads, on social media platforms. Utilizing the number of views, comments, and shares as indicators of diffusion, we developed and validated an original research framework based on the big data approach (using all the blog posts in a university in the year 2013; N = 4120). This A-B-C framework (1) analyzes the textual features of blog posts using linguistic inquiry and word count (Study 1), (2) applies the former results to build message concepts (Study 2), and (3) creates validated instructional material based on message concepts to promote message diffusion among blog readers (Study 3). This framework supports operational strategies for developing strategic and corporate communication material aimed at increasing diffusion.

Pro- and anti-opioid use posts on Reddit: A Content Analysis (Preprint)

2020 ◽  
Author(s):  
Shreya Godishala ◽  
Anna Jolliff
Keyword(s):  
United States ◽  
Social Media ◽  
Content Analysis ◽  
Health Risks ◽  
Online Community ◽  
The United States ◽  
Word Count ◽  
Opioid Use ◽  
Linguistic Inquiry ◽  
Variable Focus

BACKGROUND Opioid use is a leading cause of injury-related deaths in the United States. Past studies have shown that analyzing opioid-related social media has the potential to reveal patterns of opioid abuse offline. OBJECTIVE The purpose of this study is to examine how users are posting about pro and anti- opioid use on Reddit. METHODS There were 100 posts selected from the Reddit online community r/Opioids. The comments and upvotes for each of the posts were collected. The posts were also run through Linguistic Inquiry and Word Count software with 16 variables. RESULTS There were on average more comments for anti- opioid posts (M= 9.06 , SD =12.22) than for the average number of comments for pro- opioid posts (M=14.88, SD= 17.89) t(81)= (2.79), p = 0.0065 < 0.05. There were on average more upvotes for anti- opioid posts (M= 1.58 , SD =1.98) than for the average number of comments for pro- opioid posts (M= 41.67, SD= 16.36), t(81)= (16.84), p = 0.0001. For LIWC variable Focus Present was found to be more significant in anti- opioid posts (M= 11.19, SD =5.79) than for pro- opioid posts (M= 15.58, SD=9.81), t(98)= (2.82), p =.0207. Focus future was also found to be more significant in anti- opioid posts (M= 1.63 , SD =1.86) than for pro- opioid posts (M=1.63, SD= 1.86), t(98)= (2.84), p = .0244. CONCLUSIONS Although there were on average more pro-opioid use posts in the online community, there seemed to be more engagement and support for the anti-opioid use posts. For the LIWC variables that were analyzed, Focus Present and Focus Future were found to be significant. This suggests that some users may be more likely to use vocabulary pertaining to future and present situations while looking at the health risks of opioids.

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