RepPer: Perception of Psychiatric Disorders on Twitter in French (Preprint)

2020 ◽  
Author(s):  
Sarah Delanys ◽  
Farah Benamara ◽  
Véronique Moriceau ◽  
François Olivier ◽  
Josiane Mothe
Keyword(s):  
Social Media ◽  
Psychiatric Disorders ◽  
Digital Technology ◽  
Psychotic Disorders ◽  
Negative Polarity ◽  
Machine Learning Algorithms ◽  
Annotation Scheme ◽  
Word Use ◽  
Wide Range ◽  
Initial Dataset

BACKGROUND With the advent of digital technology and specifically user generated contents in social media, new ways emerged for studying possible stigma of people in relation with mental health. Several pieces of work studied the discourse conveyed about psychiatric pathologies on Twitter considering mostly tweets in English and a limited number of psychiatric disorders terms. This paper proposes the first study to analyze the use of a wide range of psychiatric terms in tweets in French. OBJECTIVE Our aim is to study how generic, nosographic and therapeutic psychiatric terms are used on Twitter in French. More specifically, our study has three complementary goals: (1) to analyze the types of psychiatric word use namely medical, misuse, irrelevant, (2) to analyze the polarity conveyed in the tweets that use these terms (positive/negative/neural), and (3) to compare the frequency of these terms to those observed in related work (mainly in English ). METHODS Our study has been conducted on a corpus of tweets in French posted between 01/01/2016 to 12/31/2018 and collected using dedicated keywords. The corpus has been manually annotated by clinical psychiatrists following a multilayer annotation scheme that includes the type of word use and the opinion orientation of the tweet. Two analysis have been performed. First a qualitative analysis to measure the reliability of the produced manual annotation, then a quantitative analysis considering mainly term frequency in each layer and exploring the interactions between them. RESULTS One of the first result is a resource as an annotated dataset . The initial dataset is composed of 22,579 tweets in French containing at least one of the selected psychiatric terms. From this set, experts in psychiatry randomly annotated 3,040 tweets that corresponds to the resource resulting from our work. The second result is the analysis of the annotations; it shows that terms are misused in 45.3% of the tweets and that their associated polarity is negative in 86.2% of the cases. When considering the three types of term use, 59.5% of the tweets are associated to a negative polarity. Misused terms related to psychotic disorders (55.5%) are more frequent to those related to mood disorders (26.5%). CONCLUSIONS Some psychiatric terms are misused in the corpora we studied; which is consistent with the results reported in related work in other languages. Thanks to the great diversity of studied terms, this work highlighted a disparity in the representations and ways of using psychiatric terms. Moreover, our study is important to help psychiatrists to be aware of the term use in new communication media such as social networks which are widely used. This study has the huge advantage to be reproducible thanks to the framework and guidelines we produced; so that the study could be renewed in order to analyze the evolution of term usage. While the newly build dataset is a valuable resource for other analytical studies, it could also serve to train machine learning algorithms to automatically identify stigma in social media.

scholarly journals Classification of unlabeled online media

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Sakthi Kumar Arul Prakash ◽  
Conrad Tucker
Keyword(s):  
Social Media ◽  
Real World ◽  
Graphical Model ◽  
Ground Truth ◽  
Classification Problem ◽  
Machine Learning Algorithms ◽  
Social Media Networks ◽  
Online Social Media ◽  
Wide Range

AbstractThis work investigates the ability to classify misinformation in online social media networks in a manner that avoids the need for ground truth labels. Rather than approach the classification problem as a task for humans or machine learning algorithms, this work leverages user–user and user–media (i.e.,media likes) interactions to infer the type of information (fake vs. authentic) being spread, without needing to know the actual details of the information itself. To study the inception and evolution of user–user and user–media interactions over time, we create an experimental platform that mimics the functionality of real-world social media networks. We develop a graphical model that considers the evolution of this network topology to model the uncertainty (entropy) propagation when fake and authentic media disseminates across the network. The creation of a real-world social media network enables a wide range of hypotheses to be tested pertaining to users, their interactions with other users, and with media content. The discovery that the entropy of user–user and user–media interactions approximate fake and authentic media likes, enables us to classify fake media in an unsupervised learning manner.

scholarly journals Looking for Razors and Needles in a Haystack: Multifaceted Analysis of Suicidal Declarations on Social Media—A Pragmalinguistic Approach

2021 ◽  
Vol 18 (22) ◽  
pp. 11759
Author(s):  
Michal Ptaszynski ◽  
Monika Zasko-Zielinska ◽  
Michal Marcinczuk ◽  
Gniewosz Leliwa ◽  
Marcin Fortuna ◽  
...  
Keyword(s):  
Social Media ◽  
High Performance ◽  
Large Scale ◽  
Annotation Scheme ◽  
Contextual Processing ◽  
Wide Range ◽  
Before And After ◽  
Media Platform ◽  
Future Work ◽  
Multifaceted Analysis

In this paper, we study language used by suicidal users on Reddit social media platform. To do that, we firstly collect a large-scale dataset of Reddit posts and annotate it with highly trained and expert annotators under a rigorous annotation scheme. Next, we perform a multifaceted analysis of the dataset, including: (1) the analysis of user activity before and after posting a suicidal message, and (2) a pragmalinguistic study on the vocabulary used by suicidal users. In the second part of the analysis, we apply LIWC, a dictionary-based toolset widely used in psychology and linguistic research, which provides a wide range of linguistic category annotations on text. However, since raw LIWC scores are not sufficiently reliable, or informative, we propose a procedure to decrease the possibility of unreliable and misleading LIWC scores leading to misleading conclusions by analyzing not each category separately, but in pairs with other categories. The analysis of the results supported the validity of the proposed approach by revealing a number of valuable information on the vocabulary used by suicidal users and helped to pin-point false predictors. For example, we were able to specify that death-related words, typically associated with suicidal posts in the majority of the literature, become false predictors, when they co-occur with apostrophes, even in high-risk subreddits. On the other hand, the category-pair based disambiguation helped to specify that death becomes a predictor only when co-occurring with future-focused language, informal language, discrepancy, or 1st person pronouns. The promising applicability of the approach was additionally analyzed for its limitations, where we found out that although LIWC is a useful and easily applicable tool, the lack of any contextual processing makes it unsuitable for application in psychological and linguistic studies. We conclude that disadvantages of LIWC can be easily overcome by creating a number of high-performance AI-based classifiers trained for annotation of similar categories as LIWC, which we plan to pursue in future work.

What makes the difference? Employee social media brand engagement

2019 ◽  
Vol 34 (7) ◽  
pp. 1459-1467 ◽  
Author(s):  
Sherese Y. Duncan ◽  
Raeesah Chohan ◽  
João José Ferreira
Keyword(s):  
Social Media ◽  
Brand Equity ◽  
Positive Emotions ◽  
Content Type ◽  
Word Use ◽  
Brand Engagement ◽  
Analytical Thinking ◽  
Industry Competition ◽  
The Difference ◽  
Business To Business

Purpose This paper aims to explore, using the employee lens of business-to-business firms, word use through brand engagement and social media interaction to understand the difference between employees who rate their employer brands highly on social media and those who don't. Design/methodology/approach We conducted a textual content analysis of posts published on the social media job evaluation site glassdoor.com. LIWC software package was used to analyze 30 of the top 200 business-to-business brands listed on Brandwatch using four variables, namely, analytical thinking, clout, authenticity and emotional tone. Findings The results show that employees who rate their employer’s brand low use significantly more words, are significantly less analytic and write with significantly more clout because they focus more on others than themselves. Employees who rate their employer’s brand highly, write with significantly more authenticity, exhibit a significantly higher tone and display far more positive emotions in their reviews. Practical implications Brand managers should treat social media data disseminated by individual stakeholders, like the variables used in this study (tone, word count, frequency), as a valuable tool for brand insight on their industry, competition and their own brand equity, now and especially over time. Originality/value This study provides acknowledgement that social media is a significant source of marketing intelligence that may improve brand equity by better understanding and managing brand engagement.

scholarly journals Early Dropout Prediction in MOOCs through Supervised Learning and Hyperparameter Optimization

Electronics ◽  
2021 ◽  
Vol 10 (14) ◽  
pp. 1701
Author(s):  
Theodor Panagiotakopoulos ◽  
Sotiris Kotsiantis ◽  
Georgios Kostopoulos ◽  
Omiros Iatrellis ◽  
Achilles Kameas
Keyword(s):  
Online Education ◽  
Online Courses ◽  
Early Stage ◽  
Activity Patterns ◽  
Daily Basis ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Educational Institutions ◽  
Student Dropout ◽  
Wide Range

Over recent years, massive open online courses (MOOCs) have gained increasing popularity in the field of online education. Students with different needs and learning specificities are able to attend a wide range of specialized online courses offered by universities and educational institutions. As a result, large amounts of data regarding students’ demographic characteristics, activity patterns, and learning performances are generated and stored in institutional repositories on a daily basis. Unfortunately, a key issue in MOOCs is low completion rates, which directly affect student success. Therefore, it is of utmost importance for educational institutions and faculty members to find more effective practices and reduce non-completer ratios. In this context, the main purpose of the present study is to employ a plethora of state-of-the-art supervised machine learning algorithms for predicting student dropout in a MOOC for smart city professionals at an early stage. The experimental results show that accuracy exceeds 96% based on data collected during the first week of the course, thus enabling effective intervention strategies and support actions.

Decolonizing Science, Digitizing the Occult: Theory from the Virtual South

2021 ◽  
Vol 64 (1) ◽  
pp. 86-104
Author(s):  
Sasha Newell
Keyword(s):  
Social Media ◽  
Intimate Relationships ◽  
Digital Technology ◽  
World Wide ◽  
Western Science ◽  
The World ◽  
University Of Cape Town ◽  
The University ◽  
Second World ◽  
Forms Of Knowledge

AbstractIn this article Newell uses two case studies to explore one of the central threads of Mbembe’s Abiola lecture, the idea that there is a relationship between the plasticity of digital technology and African cosmologies of the deuxième monde. One case concerns the viral YouTube video #sciencemustfall, in which students at the University of Cape Town criticize “Western” science and demand that African forms of knowledge such as witchcraft be incorporated into the meaning of science. The second case considers fieldwork among the brouteurs of Côte d’Ivoire, internet scammers who build intimate relationships on false premises using social media. They acquire shocking amounts of wealth in this way which they display on their own social media accounts. However, they are said to use occult means to seduce and persuade their virtual lovers, trapping their prey in the sticky allure of the world wide web. Newell uses both examples to highlight the overlaps between the transformational efficacies embedded in both occult ontologies and digital worldings, calling for the possibility of using African cosmologies of the second world to produce a ‘theory from the south’ of virtual sociality.

scholarly journals Detecting cybersecurity attacks across different network features and learners

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Joffrey L. Leevy ◽  
John Hancock ◽  
Richard Zuech ◽  
Taghi M. Khoshgoftaar
Keyword(s):  
Feature Selection ◽  
Intrusion Detection ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Machine Learning Algorithms ◽  
Feature Selection Technique ◽  
Impact Performance ◽  
Detection Model ◽  
Wide Range ◽  
Research Questions

AbstractMachine learning algorithms efficiently trained on intrusion detection datasets can detect network traffic capable of jeopardizing an information system. In this study, we use the CSE-CIC-IDS2018 dataset to investigate ensemble feature selection on the performance of seven classifiers. CSE-CIC-IDS2018 is big data (about 16,000,000 instances), publicly available, modern, and covers a wide range of realistic attack types. Our contribution is centered around answers to three research questions. The first question is, “Does feature selection impact performance of classifiers in terms of Area Under the Receiver Operating Characteristic Curve (AUC) and F1-score?” The second question is, “Does including the Destination_Port categorical feature significantly impact performance of LightGBM and Catboost in terms of AUC and F1-score?” The third question is, “Does the choice of classifier: Decision Tree (DT), Random Forest (RF), Naive Bayes (NB), Logistic Regression (LR), Catboost, LightGBM, or XGBoost, significantly impact performance in terms of AUC and F1-score?” These research questions are all answered in the affirmative and provide valuable, practical information for the development of an efficient intrusion detection model. To the best of our knowledge, we are the first to use an ensemble feature selection technique with the CSE-CIC-IDS2018 dataset.

scholarly journals Relationship Pattern of Personality Disorder Traits in Major Psychiatric Disorders: A Cross-Sectional Study

2021 ◽  
pp. 025371762199953
Author(s):  
Bhavneesh Saini ◽  
Pir Dutt Bansal ◽  
Mamta Bahetra ◽  
Arvind Sharma ◽  
Priyanka Bansal ◽  
...  
Keyword(s):  
Personality Disorder ◽  
Psychiatric Disorders ◽  
Psychotic Disorders ◽  
Age Of Onset ◽  
Psychiatric Patients ◽  
Cross Sectional Study ◽  
Psychotic Symptoms ◽  
Sectional Study ◽  
Cross Sectional ◽  
Neurotic Disorders

Background: Normal personality development, gone awry due to genetic or environmental factors, results in personality disorders (PD). These often coexist with other psychiatric disorders, affecting their outcome adversely. Considering the heterogeneity of data, more research is warranted. Methods: This was a cross-sectional study on personality traits in psychiatric patients of a tertiary hospital, over 1 year. Five hundred and twenty-five subjects, aged 18–45 years, with substance, psychotic, mood, or neurotic disorders were selected by convenience sampling. They were evaluated for illness-related variables using psychiatric pro forma; diagnostic confirmation and severity assessment were done using ICD-10 criteria and suitable scales. Personality assessment was done using the International Personality Disorder Examination after achieving remission. Results: Prevalence of PD traits and PDs was 56.3% and 4.2%, respectively. While mood disorders were the diagnostic group with the highest prevalence of PD traits, it was neurotic disorders for PDs. Patients with PD traits had a past psychiatric history and upper middle socioeconomic status (SES); patients with PDs were urban and unmarried. Both had a lower age of onset of psychiatric illness. Psychotic patients with PD traits had higher and lower PANSS positive and negative scores, respectively. The severity of personality pathology was highest for mixed cluster and among neurotic patients. Clusterwise prevalence was cluster C > B > mixed > A (47.1%, 25.2%, 16.7%, and 11.4%). Among subtypes, anankastic (18.1%) and mixed (16.7%) had the highest prevalence. Those in the cluster A group were the least educated and with lower SES than others. Conclusions: PD traits were present among 56.3% of the patients, and they had many significant sociodemographic and illness-related differences from those without PD traits. Cluster C had the highest prevalence. Among patients with psychotic disorders, those with PD traits had higher severity of psychotic symptoms.

scholarly journals Activity in the brain’s valuation and mentalizing networks is associated with propagation of online recommendations

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Elisa C. Baek ◽  
Matthew Brook O’Donnell ◽  
Christin Scholz ◽  
Rui Pei ◽  
Javier O. Garcia ◽  
...  
Keyword(s):  
Social Media ◽  
Word Of Mouth ◽  
Mental States ◽  
Brain Regions ◽  
Prior Work ◽  
Opinion Change ◽  
Wide Range ◽  
The Mind ◽  
Mentalizing System

AbstractWord of mouth recommendations influence a wide range of choices and behaviors. What takes place in the mind of recommendation receivers that determines whether they will be successfully influenced? Prior work suggests that brain systems implicated in assessing the value of stimuli (i.e., subjective valuation) and understanding others’ mental states (i.e., mentalizing) play key roles. The current study used neuroimaging and natural language classifiers to extend these findings in a naturalistic context and tested the extent to which the two systems work together or independently in responding to social influence. First, we show that in response to text-based social media recommendations, activity in both the brain’s valuation system and mentalizing system was associated with greater likelihood of opinion change. Second, participants were more likely to update their opinions in response to negative, compared to positive, recommendations, with activity in the mentalizing system scaling with the negativity of the recommendations. Third, decreased functional connectivity between valuation and mentalizing systems was associated with opinion change. Results highlight the role of brain regions involved in mentalizing and positive valuation in recommendation propagation, and further show that mentalizing may be particularly key in processing negative recommendations, whereas the valuation system is relevant in evaluating both positive and negative recommendations.

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