Depression Detection from Social Media Using Twitter’s Tweet

Lia, Rifat Jahan; Siddikk, Abu Bakkar; Muntasir, Fahim; Rahman, Sheikh Shah Mohammad Motiur; Jahan, Nusrat

doi:10.1007/978-3-030-87954-9_9

Cited by 7 publications

(8 citation statements)

References 30 publications

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“…Their results showed that the correlation-based method leads to better outcomes. Lia et al (2022) used six generic machine learning algorithms to detect depression from Twitter’s tweets. They used term frequency-inverse document frequency for feature extraction before applying classification algorithms.…”

Section: Related Workmentioning

confidence: 99%

An optimized deep learning approach for suicide detection through Arabic tweets

Baghdadi

Malki

Balaha

et al. 2022

PeerJ Computer Science

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Many people worldwide suffer from mental illnesses such as major depressive disorder (MDD), which affect their thoughts, behavior, and quality of life. Suicide is regarded as the second leading cause of death among teenagers when treatment is not received. Twitter is a platform for expressing their emotions and thoughts about many subjects. Many studies, including this one, suggest using social media data to track depression and other mental illnesses. Even though Arabic is widely spoken and has a complex syntax, depressive detection methods have not been applied to the language. The Arabic tweets dataset should be scraped and annotated first. Then, a complete framework for categorizing tweet inputs into two classes (such as Normal or Suicide) is suggested in this study. The article also proposes an Arabic tweet preprocessing algorithm that contrasts lemmatization, stemming, and various lexical analysis methods. Experiments are conducted using Twitter data scraped from the Internet. Five different annotators have annotated the data. Performance metrics are reported on the suggested dataset using the latest Bidirectional Encoder Representations from Transformers (BERT) and Universal Sentence Encoder (USE) models. The measured performance metrics are balanced accuracy, specificity, F1-score, IoU, ROC, Youden Index, NPV, and weighted sum metric (WSM). Regarding USE models, the best-weighted sum metric (WSM) is 80.2%, and with regards to Arabic BERT models, the best WSM is 95.26%.

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Section: Related Workmentioning

confidence: 99%

An optimized deep learning approach for suicide detection through Arabic tweets

Baghdadi

Malki

Balaha

et al. 2022

PeerJ Computer Science

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“…[ 6 ] studied depression in tweets in Spain. In fact, studies [ 3 , 4 , 5 , 15 ] studied depression. Furthermore, ref.…”

Section: Literature Review: Anxiety and Depression In Snmentioning

confidence: 99%

“…[ 3 ] used Facebook ref. [ 4 ] used Twitter. In [ 9 ], the researchers conducted a systematic review of 70 published studies that discussed social networks (SN) with depression and anxiety.…”

Section: Literature Review: Anxiety and Depression In Snmentioning

confidence: 99%

“…Others use SNs for professional networking, such as LinkedIn, as well as for scientific research, such as ResearchGate. Still, negative consequences related to SNs are presented in many studies that relate the use of social networks to depression [ 1 , 2 , 3 , 4 , 5 , 6 , 7 ], anxiety [ 8 , 9 , 10 ], addiction [ 11 , 12 ], loneliness, stress [ 13 , 14 ], mental illness [ 15 ], social isolation [ 16 ] and many negative connotations as shall be seen in the coming sections.…”

Section: Introductionmentioning

confidence: 99%

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Factors Affecting the Use of Social Networks and Its Effect on Anxiety and Depression among Parents and Their Children: Predictors Using ML, SEM and Extended TAM

Abu-Taieh

AlHadid

Masa’deh

et al. 2022

IJERPH

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Previous research has found support for depression and anxiety associated with social networks. However, little research has explored parents’ depression and anxiety constructs as mediators that may account for children’s depression and anxiety. The purpose of this paper is to test the influence of different factors on children’s depression and anxiety, extending from parents’ anxiety and depression in Jordan. The authors recruited 857 parents to complete relevant web survey measures with constructs and items and a model based on different research models TAM and extended with trust, analyzed using SEM, CFA with SPSS and AMOS, and ML methods, using the triangulation method to validate the results and help predict future applications. The authors found support for the structural model whereby behavioral intention to use social media influences the parent’s anxiety and depression which correlate to their offspring’s anxiety and depression. Behavioral intention to use social media can be enticed by enjoyment, trust, ease of use, usefulness, and social influences. This study is unique in exploring rumination in the context of the relationship between parent–child anxiety and depression due to the use of social networks.

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“…Standardization and comparative performance evaluation are needed in market prediction. Lia et al [48] proposed a realtime monitoring tool for changing customer needs investigation from product planning from social media mining. The approach applied to only one target product where analysis in different disciplines can be done.…”

Section: Introductionmentioning

confidence: 99%

Social crisis detection using Twitter based text mining-a machine learning approach

Rahman

Jahan²,

Sadia³

et al. 2023

Bulletin EEI

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Social-media and blogs are increasingly used for social-communication, an idea and thought publishing platform. Public intentions, wisdom, problems, solutions, mental states are shared in social media. Text is being the best and the most common way to communicate over social networks. All kinds of data shared in social sites like Facebook, Twitter, and Microblogs. People from different pursuance uses these media to publish thoughts and convey messages through text. Consequently, occurrences in social life are rapidly discussed in social blogs in daily manner. This work aims at discovering ongoing social crisis from the Twitter data. Text mining technique and sentiment analysis were applied to detect the current social crisis from the social sites. Twitter data were collected to identify the recent social crisis. Furthermore, the identified crisis was compared to reputed newspapers. A hybrid method used to detect recent social issues resulted nicely. However, our proposed analysis shows identifying rate 89%, 95%, 83%, 53%, and 98% for the top 5 identified crisis accordingly in the date between 27 February and 11 March 2020. The strategy used in this study for the detection of recent social crisis will contribute to the social life and findings of crisis will be eliminated easily.

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Depression Detection from Social Media Using Twitter’s Tweet

Cited by 7 publications

References 30 publications

An optimized deep learning approach for suicide detection through Arabic tweets

An optimized deep learning approach for suicide detection through Arabic tweets

Factors Affecting the Use of Social Networks and Its Effect on Anxiety and Depression among Parents and Their Children: Predictors Using ML, SEM and Extended TAM

Social crisis detection using Twitter based text mining-a machine learning approach

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