Characterization of depression in Spanish tweets: a behavioral and linguistic analysis (Preprint)

2019 ◽  
Author(s):  
Angela Leis ◽  
Francesco Ronzano ◽  
Miguel A. Mayer ◽  
Laura I. Furlong ◽  
Ferran Sanz
Keyword(s):  
Social Media ◽  
Mental Disorders ◽  
Healthcare Services ◽  
Linguistic Features ◽  
Second Person ◽  
Daily Lives ◽  
Control Dataset ◽  
Social Media Platforms ◽  
Twitter Users ◽  
Selection Of

BACKGROUND Mental disorders have become a major concern in public health and are one of the main causes of the overall disease burden worldwide. Social media platforms allow us to observe the activities, thoughts and feelings of people’s daily lives, including those of patients suffering from mental disorders. There are studies that have analyzed the influence of mental disorders, including depression, in the behavior of social media users, but they have been usually focused on messages written in English. OBJECTIVE The aim of this study is to identify the linguistic features of tweets in Spanish and the behavioral patterns of Twitter users that generate them, which could suggest signs of depression. METHODS This study was developed in two steps. In the first step, the selection of users and the compilation of tweets were performed. Three datasets of tweets were created, a depressive users dataset (made up of the timeline of 90 users who explicitly mention that they suffer from depression), a depressive tweets dataset (a manually curated selection of tweets from the previous users that include expressions indicative of depression) and a control dataset (made up of the timeline of 450 randomly selected users). In the second step, the comparison and analysis of the three datasets of tweets were carried out. RESULTS In comparison to the control dataset, the depressive users are less active in posting tweets, doing it more frequently between 23:00 and 6:00 (P<.001). The percentage of nouns used by the control dataset almost doubles that of the depressive users (P<.001). By contrast, the use of verbs is more common in the depressive users dataset (P<.001). The first-person singular pronoun was by far the most used in the depressive users dataset (80%) and the first and the second person plural were the less frequent (0.4% in both cases), being this distribution different to that of the control dataset (P<.001). Sadness and anger emotions were the most common in the depressive users and depressive tweets datasets with significant differences when comparing these datasets with the control one (P<.001). As for negation words, they were detected in the 34% and 46% of the tweets in the depressive users and depressive tweets respectively, which are significantly different to the control dataset (P<.001). Negative polarity was more frequent in the depressive users (54%) and depressive tweets (65%) datasets than in the control one (43.5%) (P<.001). CONCLUSIONS Twitter users who are potentially suffering from depression modify the general characteristics of their language and the way they interact on social media. Based on these changes these users can be monitored and supported, thus introducing new opportunities for the study of depression and for providing additional healthcare services to people with this disorder.

scholarly journals Towards a Stylometric Authorship Recognition Model for the Social Media Texts in Arabic

2020 ◽  
Vol 11 (4) ◽  
pp. 490-507
Author(s):  
Haroon Nasser Alsager
Keyword(s):  
Social Media ◽  
Vector Space ◽  
Complete Information ◽  
Clustering Methods ◽  
Linguistic Features ◽  
The Social ◽  
Social Media Platforms ◽  
Recognition Systems ◽  
Twitter Users ◽  
Degree Of Overlap

Numerous studies have been concerned with developing new authorship recognition systems to address the increasing rates of cybercrimes associated with the anonymous nature of social media platforms, which still offer the opportunity for the users not to reveal their true identities. Nevertheless, it is still challenging to identify the real authors of social media’s offensive and inappropriate content. These contents are usually very short; therefore, it is challenging for stylometric authorship systems to assign controversial texts to their real authors based on the salient and distinctive linguistic features and patterns within these contents. This research introduces a new stylometric authorship system that considers both the shortness of data and the peculiar linguistic properties of Arabic. A corpus of 20, 357 tweets from 134 Twitter users. A document clustering based on Document Index Graph (DIG) model was used to classify input patterns in the tweets that shared common linguistic features. A comparative analysis using Vector Space Clustering (VSC) model based on the Bag of Words (BOW) model, conventionally used in authorship recognition applications, was used. Results indicate that the proposed system is more accurate than other standard authorship systems mainly based on vector space clustering methods. It was also clear that the model had the advantage of providing complete information about the documents and the degree of overlap between every pair of documents, which was useful in determining the similarity between documents.

scholarly journals COVID-19 Related Sentiment Analysis Using State-of-the-Art Machine Learning and Deep Learning Techniques

2022 ◽  
Vol 9 ◽  
Author(s):  
Zunera Jalil ◽  
Ahmed Abbasi ◽  
Abdul Rehman Javed ◽  
Muhammad Badruddin Khan ◽  
Mozaherul Hoque Abul Hasanat ◽  
...  
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Deep Learning ◽  
Sentiment Analysis ◽  
Social Networking Sites ◽  
Daily Lives ◽  
The Public ◽  
Learning Techniques ◽  
Social Media Platforms ◽  
Twitter Users

The coronavirus disease 2019 (COVID-19) pandemic has influenced the everyday life of people around the globe. In general and during lockdown phases, people worldwide use social media network to state their viewpoints and general feelings concerning the pandemic that has hampered their daily lives. Twitter is one of the most commonly used social media platforms, and it showed a massive increase in tweets related to coronavirus, including positive, negative, and neutral tweets, in a minimal period. The researchers move toward the sentiment analysis and analyze the various emotions of the public toward COVID-19 due to the diverse nature of tweets. Meanwhile, people have expressed their feelings regarding the vaccinations' safety and effectiveness on social networking sites such as Twitter. As an advanced step, in this paper, our proposed approach analyzes COVID-19 by focusing on Twitter users who share their opinions on this social media networking site. The proposed approach analyzes collected tweets' sentiments for sentiment classification using various feature sets and classifiers. The early detection of COVID-19 sentiments from collected tweets allow for a better understanding and handling of the pandemic. Tweets are categorized into positive, negative, and neutral sentiment classes. We evaluate the performance of machine learning (ML) and deep learning (DL) classifiers using evaluation metrics (i.e., accuracy, precision, recall, and F1-score). Experiments prove that the proposed approach provides better accuracy of 96.66, 95.22, 94.33, and 93.88% for COVISenti, COVIDSenti_A, COVIDSenti_B, and COVIDSenti_C, respectively, compared to all other methods used in this study as well as compared to the existing approaches and traditional ML and DL algorithms.

scholarly journals Towards a Stylometric Authorship Recognition Model for the Social Media Texts in Arabic

2021 ◽  
Author(s):  
Haroon Nasser Alsager
Keyword(s):  
Social Media ◽  
Vector Space ◽  
Complete Information ◽  
Clustering Methods ◽  
Linguistic Features ◽  
The Social ◽  
Social Media Platforms ◽  
Recognition Systems ◽  
Twitter Users ◽  
Degree Of Overlap

Numerous studies have been concerned with developing new authorship recognition systems to address the increasing rates of cybercrimes associated with the anonymous nature of social media platforms, which still offer the opportunity for the users not to reveal their true identities. Nevertheless, it is still challenging to identify the real authors of social media’s offensive and inappropriate content. These contents are usually very short; therefore, it is challenging for stylometric authorship systems to assign controversial texts to their real authors based on the salient and distinctive linguistic features and patterns within these contents. This research introduces a new stylometric authorship system that considers both the shortness of data and the peculiar linguistic properties of Arabic. A corpus of 20, 357 tweets from 134 Twitter users. A document clustering based on Document Index Graph (DIG) model was used to classify input patterns in the tweets that shared common linguistic features. A comparative analysis using Vector Space Clustering (VSC) model based on the Bag of Words (BOW) model, conventionally used in authorship recognition applications, was used. Results indicate that the proposed system is more accurate than other standard authorship systems mainly based on vector space clustering methods. It was also clear that the model had the advantage of providing complete information about the documents and the degree of overlap between every pair of documents, which was useful in determining the similarity between documents.

Comparing Representation of Depression on Reddit and Twitter Social Media Platforms (Preprint)

2020 ◽  
Author(s):  
Ethan Kaji ◽  
Maggie Bushman
Keyword(s):  
Mental Health ◽  
Social Media ◽  
Content Analysis ◽  
Relative Frequency ◽  
Social Activity ◽  
Social Media Platforms ◽  
Dsm Iv ◽  
Twitter Users

BACKGROUND Adolescents with depression often turn to social media to express their feelings, for support, and for educational purposes. Little is known about how Reddit, a forum-based platform, compares to Twitter, a newsfeed platform, when it comes to content surrounding depression. OBJECTIVE The purpose of this study is to identify differences between Reddit and Twitter concerning how depression is discussed and represented online. METHODS A content analysis of Reddit posts and Twitter posts, using r/depression and #depression, identified signs of depression using the DSM-IV criteria. Other youth-related topics, including School, Family, and Social Activity, and the presence of medical or promotional content were also coded for. Relative frequency of each code was then compared between platforms as well as the average DSM-IV score for each platform. RESULTS A total of 102 posts were included in this study, with 53 Reddit posts and 49 Twitter posts. Findings suggest that Reddit has more content with signs of depression with 92% than Twitter with 24%. 28.3% of Reddit posts included medical content compared to Twitter with 18.4%. 53.1% of Twitter posts had promotional content while Reddit posts didn’t contain promotional content. CONCLUSIONS Users with depression seem more willing to discuss their mental health on the subreddit r/depression than on Twitter. Twitter users also use #depression with a wider variety of topics, not all of which actually involve a case of depression.

A Survey of Computational Methods for Online Mental State Assessment on Social Media

2021 ◽  
Vol 2 (2) ◽  
pp. 1-31
Author(s):  
Esteban A. Ríssola ◽  
David E. Losada ◽  
Fabio Crestani
Keyword(s):  
Social Media ◽  
Mental Disorders ◽  
Mental State ◽  
Extraction Process ◽  
State Assessment ◽  
Future Research ◽  
Linguistic Markers ◽  
Online Social Media ◽  
Suicide Behaviour ◽  
Social Media Platforms

Mental state assessment by analysing user-generated content is a field that has recently attracted considerable attention. Today, many people are increasingly utilising online social media platforms to share their feelings and moods. This provides a unique opportunity for researchers and health practitioners to proactively identify linguistic markers or patterns that correlate with mental disorders such as depression, schizophrenia or suicide behaviour. This survey describes and reviews the approaches that have been proposed for mental state assessment and identification of disorders using online digital records. The presented studies are organised according to the assessment technology and the feature extraction process conducted. We also present a series of studies which explore different aspects of the language and behaviour of individuals suffering from mental disorders, and discuss various aspects related to the development of experimental frameworks. Furthermore, ethical considerations regarding the treatment of individuals’ data are outlined. The main contributions of this survey are a comprehensive analysis of the proposed approaches for online mental state assessment on social media, a structured categorisation of the methods according to their design principles, lessons learnt over the years and a discussion on possible avenues for future research.

Artificial Intelligence, Social Media and Depression. ‘Patient’ Autonomy Revisited

2021 ◽  
Vol 66 (Special Issue) ◽  
pp. 133-133
Author(s):  
Regina Mueller ◽  
◽  
Sebastian Laacke ◽  
Georg Schomerus ◽  
Sabine Salloch ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Social Media ◽  
Mental Disorders ◽  
Patient Autonomy ◽  
Ethical Challenges ◽  
Traditional Concept ◽  
Ethical Implications ◽  
Health Related ◽  
Twitter Users ◽  
Using Data

"Artificial Intelligence (AI) systems are increasingly being developed and various applications are already used in medical practice. This development promises improvements in prediction, diagnostics and treatment decisions. As one example, in the field of psychiatry, AI systems can already successfully detect markers of mental disorders such as depression. By using data from social media (e.g. Instagram or Twitter), users who are at risk of mental disorders can be identified. This potential of AI-based depression detectors (AIDD) opens chances, such as quick and inexpensive diagnoses, but also leads to ethical challenges especially regarding users’ autonomy. The focus of the presentation is on autonomy-related ethical implications of AI systems using social media data to identify users with a high risk of suffering from depression. First, technical examples and potential usage scenarios of AIDD are introduced. Second, it is demonstrated that the traditional concept of patient autonomy according to Beauchamp and Childress does not fully account for the ethical implications associated with AIDD. Third, an extended concept of “Health-Related Digital Autonomy” (HRDA) is presented. Conceptual aspects and normative criteria of HRDA are discussed. As a result, HRDA covers the elusive area between social media users and patients. "

scholarly journals Habib Husein Jafar Al-Hadar’s Da’wa Content Commodification on Youtube

2021 ◽  
Vol 15 (1) ◽  
pp. 1-11
Author(s):  
Akhmad Roja Badrus Zaman ◽  
Mahin Muqaddam Assarwani
Keyword(s):  
Social Media ◽  
Young Person ◽  
Social Media Platform ◽  
Virtual Ethnography ◽  
The Social ◽  
People’S Perception ◽  
Social Media Platforms ◽  
Media Platform ◽  
Definition Of ◽  
Selection Of

Advances in technology and information provide new opportunities for preachers to be able to take part in spreading Islamic teachings through various social media platforms. One of the preachers who took the role to preach through social media was Habib Husein Jafar al-Hadar. This article examines Habib Husein Jafar’s missionary activities on the social media platform he uses, Youtube. The researcher analyzes the data by observing virtually and visually (virtual ethnography) on the da’wa content displayed by Habib Husein Jafar through Youtube. The study shows that: 1) the attention to the spiritual enlightenment efforts of the younger generation is the basis of the selection of the social media platform Youtube - because based on previous research, the users of this social media platform are 18-29 years of age; 2) starting from the da’wa consumers who are primarily young people, the content they present is suitable to their needs and lifestyle and 3) by using the concept of the circuit of culture analysis, Habib Husein Jafar in various ranges can reconstruct people’s perception of one’s definition of holiness. It is not limited based on normative appearance - cloaked and sacrificed, for example - but more on the substantive side, namely by behaving and having knowledgeable skills. With the variety of content, he could visualize himself as a pious young man by not abandoning his social status as a young person.

scholarly journals Instagram Influencer Analysis for Top 5 Categories in Turkey

2021 ◽  
Vol 12 (44) ◽  
pp. 22-36
Author(s):  
Busra ERTOGRUL ◽  
Gizem KILICSIZ ◽  
Aysun BOZANTA
Keyword(s):  
Social Media ◽  
Digital Identity ◽  
Ordinary People ◽  
Daily Lives ◽  
Social Media Platforms ◽  
The Difference ◽  
Marketing Tool ◽  
Use Of Social Media ◽  
The Relationship ◽  
New Jobs

Social media platforms have become an inevitable part of our daily lives. Companies that noticed the intense use of social media platforms started to use them as a marketing tool. Even ordinary people have become famous by social media and companies have been sending their products to them to try and advertise. Many people have gained a considerable amount of money in this way and today new jobs are emerged like "Youtuber" and "Instagram Influencer". Therefore, ordinary people realized the power of social media and many people started to strength their digital identity over social media. The question raising in people’s mind is that “What is the difference between the influencers and the ordinary people who have also digital identity over social media?”. This study examined Instagram influencers for five categories namely fashion, makeup, photography, travel, and fitness in Turkey. As an exploratory study, the relationship between the influencers’ average number of posts, the number of likes, the number of views, the number of comments, number of followers, and the number of following were examined. As well as the engagement rates of the followers to the influencers were calculated. In addition, the words they mostly used in the captions of the posts were examined.

scholarly journals Royal Shakespeare Company‘s #dream40

2021 ◽  
Vol 37 (1) ◽  
pp. 207-217
Author(s):  
Clara Matheus Nogueira
Keyword(s):  
Social Media ◽  
William Shakespeare ◽  
Social Networking ◽  
English Language ◽  
Daily Lives ◽  
Royal Shakespeare Company ◽  
Teachers And Students ◽  
Social Media Platforms ◽  
School Curricula

William Shakespeare is one of the greatest authors of the English language and is present in multiple school curricula. However, reading Shakespeare in classrooms can be a challenge for both teachers and students. In schools, adaptations from literature to social media platforms, such as #dream40, a production by the Royal Shakespeare Company, remain not fully explored. In this paper, this production is presented as a possible ally in the effort of bringing the English canon closer to the students’ reality, making the Bard more engaging and accessible, since this production uses mechanics that are part of most students’ daily lives on social networking platforms, such as the hashtag that appears in the title of this production; besides, #dream40 is closely aligned with our contemporary paradigm of worldview.

scholarly journals Public Perception on Healthcare Services: Evidence from Social Media Platforms in China

2019 ◽  
Vol 16 (7) ◽  
pp. 1273 ◽  
Author(s):  
Guangyu Hu ◽  
Xueyan Han ◽  
Huixuan Zhou ◽  
Yuanli Liu
Keyword(s):  
Social Media ◽  
Language Processing ◽  
Public Perception ◽  
Content Volume ◽  
Healthcare Services ◽  
Service Improvement ◽  
Data Resource ◽  
Health Related Research ◽  
Health Related ◽  
Social Media Platforms

Social media has been used as data resource in a growing number of health-related research. The objectives of this study were to identify content volume and sentiment polarity of social media records relevant to healthcare services in China. A list of the key words of healthcare services were used to extract data from WeChat and Qzone, between June 2017 and September 2017. The data were put into a corpus, where content analyses were performed using Tencent natural language processing (NLP). The final corpus contained approximately 29 million records. Records on patient safety were the most frequently mentioned topic (approximately 8.73 million, 30.1% of the corpus), with the contents on humanistic care having received the least social media references (0.43 Million, 1.5%). Sentiment analyses showed 36.1%, 16.4%, and 47.4% of positive, neutral, and negative emotions, respectively. The doctor-patient relationship category had the highest proportion of negative contents (74.9%), followed by service efficiency (59.5%), and nursing service (53.0%). Neutral disposition was found to be the highest (30.4%) in the contents on appointment-booking services. This study added evidence to the magnitude and direction of public perceptions on healthcare services in China’s hospital and pointed to the possibility of monitoring healthcare service improvement, using readily available data in social media.

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