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"Social Media - classification"
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Public discourse and sentiment during the COVID 19 pandemic: Using Latent Dirichlet Allocation for topic modeling on Twitter
The study aims to understand Twitter users' discourse and psychological reactions to COVID-19. We use machine learning techniques to analyze about 1.9 million Tweets (written in English) related to coronavirus collected from January 23 to March 7, 2020. A total of salient 11 topics are identified and then categorized into ten themes, including \"updates about confirmed cases,\" \"COVID-19 related death,\" \"cases outside China (worldwide),\" \"COVID-19 outbreak in South Korea,\" \"early signs of the outbreak in New York,\" \"Diamond Princess cruise,\" \"economic impact,\" \"Preventive measures,\" \"authorities,\" and \"supply chain.\" Results do not reveal treatments and symptoms related messages as prevalent topics on Twitter. Sentiment analysis shows that fear for the unknown nature of the coronavirus is dominant in all topics. Implications and limitations of the study are also discussed.
Journal Article
Clinical determinants of social media use in individuals with schizophrenia
This study aimed to examine the prevalence of social media use and its association with symptoms in individuals with schizophrenia. 265 individuals with schizophrenia were assessed. Symptoms were assessed on the Positive and Negative Syndrome Scale (PANSS) and the Clinical Assessment Interview for Negative Symptoms (CAINS). Information on social media use was collected. Logistic regressions were used to explore the association between social media use and socio-demographic and clinical characteristics of the participants. Of the 265 study participants, 139 (52.5%) used social media in the last week. Fifty-six (21.1%) of the study participants used more than one social media site in the last week. Facebook was the most popular social media site. Age, highest education level, monthly household income, PANSS negative and depression factor scores were significantly associated with social media use. Amongst negative symptoms, the CAINS motivation-pleasure (MAP) social factor scores were found to be significantly associated with social media use. Our study results suggested that the assessment of social interactions via social media should be considered in the clinical assessment of individuals with schizophrenia. Secondly, our results suggested that the development of treatment programs supported by social media platforms may be useful for certain groups of individuals with schizophrenia. Younger patients with above secondary level education, higher family income and lower symptom severity are likely to be avid users of social media and would be suitable candidates to receive illness related information or clinical interventions via social media.
Journal Article
Tweeting the Meeting: Twitter Use at The American Society of Breast Surgeons Annual Meeting 2013–2016
Background
Twitter social media is being used to disseminate medical meeting information. Meeting attendees and other interested parties have the ability to follow and participate in conversations related to meeting content. We analyzed Twitter activity generated from the 2013–2016 American Society of Breast Surgeons Annual Meetings.
Methods
The Symplur Signals database was used to determine number of tweets, tweets per user, and impressions for each meeting. The number of unique physicians, patients/caregivers/advocates, and industry participants was determined. Physician tweeters were cross-referenced with membership and attendance rosters. Tweet transcripts were analyzed for content and tweets were categorized as either scientific, social, administrative, industry promotion, or irrelevant.
Results
From 2013 to 2016, the number of tweets increased by 600 %, the number of Twitter users increased by 450 %, and the number of physician tweeters increased by 457 %. The number of impressions (tweets × followers) increased from more than 3.5 million to almost 20.5 million, an increase of 469 %. The majority of tweets were informative (70–80 %); social tweets ranged from 13 to 23 %. A small percentage (3–6 %) of tweets were related to administrative matters. There were very few industry or irrelevant tweets.
Conclusions
Twitter social media use at the American Society of Breast Surgeons annual meeting showed a substantial increase during the time period evaluated. The use of Twitter during professional meetings is a tremendous opportunity to share information. The authors feel that medical conference organizers should encourage Twitter participation and should be educating attendees on the proper use of Twitter.
Journal Article
Identifying Twitter influencer profiles for health promotion in Saudi Arabia
New media platforms, such as Twitter, provide the ideal opportunity to positively influence the health of large audiences. Saudi Arabia has one of the highest number of Twitter users of any country, some of whom are very influential in setting agendas and contributing to the dissemination of ideas. Those opinion leaders, both individuals and organizations, influential in the new media environment have the potential to raise awareness of health issues, advocate for health and potentially instigate change at a social level. To realize the potential of the new media platforms for public health, the function of opinion leaders is key. This study aims to identify and profile the most influential Twitter accounts in Saudi Arabia. Multiple measures, including: number of followers and four influence scores, were used to evaluate Twitter accounts. The data were then filtered and analysed using ratio and percentage calculations to identify the most influential users. In total, 99 Saudi Twitter accounts were classified, resulting in the identification of 25 religious men/women, 16 traditional media, 14 sports related, 10 new media, 6 political, 6 company and 4 health accounts. The methods used to identify the key influential Saudi accounts can be applied to inform profile development of Twitter users in other countries.
Journal Article
Web Service Reputation Evaluation Based on QoS Measurement
In the early service transactions, quality of service (QoS) information was published by service provider which was not always true and credible. For better verification the trust of the QoS information was provided by the Web service. In this paper, the factual QoS running data are collected by our WS-QoS measurement tool; based on these objectivity data, an algorithm compares the difference of the offered and measured quality data of the service and gives the similarity, and then a reputation evaluation method computes the reputation level of the Web service based on the similarity. The initial implementation and experiment with three Web services' example show that this approach is feasible and these values can act as the references for subsequent consumers to select the service.
Journal Article
An Analysis of Yemenis' Responses and Sentiments on Social Media towards the Emergence of the COVID-19 Pandemic
Recently, many studies have widely dealt with data mining and Text classification, including sentiment analysis. Sentiment analysis (SA) is an application of Natural Language Processing (NLP) implemented to understand the public's attitudes. The recent proliferation of social media has helped gauge the public's mood. The current study aims to explore the influence of the COVID-19 pandemic on the Yemeni community and generate indices assessing public sentiments and attitudes using lexicon and rule-based approach (VAEDR: Valence Aware Dictionary and Sentiment Reasoner) and qualitative and quantitative analysis methods. 8,830 Facebook and YouTube comments were analyzed before and after the declaration of COVID-19 on 10th April 2020 in Yemen. The results revealed that sentiment polarity with and without contextual reference differed significantly. Without contextual reference, neutrality was prevalent and reached 55%; negativity scored 24% while positivity reached 21% before 10th April, but after this date, negativity was dominant and reached 57%, neutrality scored 28%, and positivity scored 15%. With contextual reference, positivity was prevalent and scored 72% before 10th April, but after this date, negativity dominated the public's mood and reached 78.23%; positivity highly decreased to 18.65%, while neutrality scored 3.12%. The study demonstrated the superiority of SA based on the contextual reference of words.
Journal Article
Studying user income through language, behaviour and affect in social media
Automatically inferring user demographics from social media posts is useful for both social science research and a range of downstream applications in marketing and politics. We present the first extensive study where user behaviour on Twitter is used to build a predictive model of income. We apply non-linear methods for regression, i.e. Gaussian Processes, achieving strong correlation between predicted and actual user income. This allows us to shed light on the factors that characterise income on Twitter and analyse their interplay with user emotions and sentiment, perceived psycho-demographics and language use expressed through the topics of their posts. Our analysis uncovers correlations between different feature categories and income, some of which reflect common belief e.g. higher perceived education and intelligence indicates higher earnings, known differences e.g. gender and age differences, however, others show novel findings e.g. higher income users express more fear and anger, whereas lower income users express more of the time emotion and opinions.
Journal Article
Sentiment analysis classification system using hybrid BERT models
Because of the rapid growth of mobile technology, social media has become an essential platform for people to express their views and opinions. Understanding public opinion can help businesses and political institutions make strategic decisions. Considering this, sentiment analysis is critical for understanding the polarity of public opinion. Most social media analysis studies divide sentiment into three categories: positive, negative, and neutral. The proposed model is a machine-learning application of a classification problem trained on three datasets. Recently, the BERT model has demonstrated effectiveness in sentiment analysis. However, the accuracy of sentiment analysis still needs to be improved. We propose four deep learning models based on a combination of BERT with Bidirectional Long ShortTerm Memory (BiLSTM) and Bidirectional Gated Recurrent Unit (BiGRU) algorithms. The study is based on pre-trained word embedding vectors that aid in the model fine-tuning process. The proposed methods are trying to enhance accuracy and check the effect of hybridizing layers of BIGRU and BILSTM on both Bert models (DistilBERT, RoBERTa) for no emoji (text sentiment classifier) and also with emoji cases. The proposed methods were compared to two pre-trained BERT models and seven other models built for the same task using classical machine learning. The proposed architectures with BiGRU layers have the best results.
Journal Article