Имидж города в многожанровом отражении публикаций социальных медиа

BackgroundSince its outbreak in January 2020, COVID-19 has quickly spread worldwide and has become a global pandemic. Social media platforms have been recognized as important tools for health-promoting practices in public health, and the use of social media is widespread among the public. However, little is known about the effects of social media use on health promotion during a pandemic such as COVID-19.ObjectiveIn this study, we aimed to explore the predictive role of social media use on public preventive behaviors in China during the COVID-19 pandemic and how disease knowledge and eHealth literacy moderated the relationship between social media use and preventive behaviors.MethodsA national web-based cross-sectional survey was conducted by a proportionate probability sampling among 802 Chinese internet users (“netizens”) in February 2020. Descriptive statistics, Pearson correlations, and hierarchical multiple regressions were employed to examine and explore the relationships among all the variables.ResultsAlmost half the 802 study participants were male (416, 51.9%), and the average age of the participants was 32.65 years. Most of the 802 participants had high education levels (624, 77.7%), had high income >¥5000 (US $736.29) (525, 65.3%), were married (496, 61.8%), and were in good health (486, 60.6%). The average time of social media use was approximately 2 to 3 hours per day (mean 2.34 hours, SD 1.11), and the most frequently used media types were public social media (mean score 4.49/5, SD 0.78) and aggregated social media (mean score 4.07/5, SD 1.07). Social media use frequency (β=.20, P<.001) rather than time significantly predicted preventive behaviors for COVID-19. Respondents were also equipped with high levels of disease knowledge (mean score 8.15/10, SD 1.43) and eHealth literacy (mean score 3.79/5, SD 0.59). Disease knowledge (β=.11, P=.001) and eHealth literacy (β=.27, P<.001) were also significant predictors of preventive behaviors. Furthermore, eHealth literacy (P=.038) and disease knowledge (P=.03) positively moderated the relationship between social media use frequency and preventive behaviors, while eHealth literacy (β=.07) affected this relationship positively and disease knowledge (β=–.07) affected it negatively. Different social media types differed in predicting an individual’s preventive behaviors for COVID-19. Aggregated social media (β=.22, P<.001) was the best predictor, followed by public social media (β=.14, P<.001) and professional social media (β=.11, P=.002). However, official social media (β=.02, P=.597) was an insignificant predictor.ConclusionsSocial media is an effective tool to promote behaviors to prevent COVID-19 among the public. Health literacy is essential for promotion of individual health and influences the extent to which the public engages in preventive behaviors during a pandemic. Our results not only enrich the theoretical paradigm of public health management and health communication but also have practical implications in pandemic control for China and other countries.

The digital age has changed humans in accessing information from offline media to online media. The presence of digital media, such as smartphone help people get current issues quickly without limits of time and place. With advances in information technology, internet users not only can receive information but also send information in the form of comments and share information. The current internet media that has become a gateway for information is social media. This paper aims to discuss information dissemination on current issues in social media. The data sources for this paper were social media texts and online questionnaire results. The research question in this paper is what current issues are communicated in social media and how is the cyber communities’ digital literacy on current issues in social media. The research findings show that 90.03% of people access information through social media, the frequency of time spent with social media to access information is 81%, and the type of social media used to access information is Facebook (38.4%), WhatsApp (20.2%), YouTube (18.4%), Twitter (8.3%) and Tiktok (6.1%). Furthermore, the current issues that can be accessed by media users are covid-19 vaccination and intolerance. The major problem with social media as a gateway to information is the digital literacy of the cyber communities on the spread of fake news related to the Covid-19 vaccination and intolerance.

INTRODUCTION. Building effective communication with the target audience through media texts is a relevant technique of interactive interaction. The correlation of methods of interactive communication with a regional audience and the functional purpose of elements of a social media media text is studied. The purpose of the study is to substantiate the convergence of the functionality of interactive methods of communication with a regional audience in media texts of social media. The theoretical novelty of the work lies in identifying the convergence of the functionality of media texts, in introducing the concept of “landing” of media content into scientific circulation.MATERIALS AND METHODS. The theoretical basis of the study was the works of L.R. Duskaeva, E.A. Zvereva, A.A. Kazakov, N.I. Klushina, A.M. Shesterina on the use of interactive techniques in media texts. The most comprehensive Tambov communities on VKontakte and Telegram channels were selected as an empirical base. The study used the method of content analysis of social media texts for 2023-2024.RESULTS AND DISCUSSION. The following conclusions were formulated in the course of the study: 1) each speech technique performs several functions, which indicates the convergence of the functionality of media texts; 2) speech techniques of interactive communication in synthesis with technical means demonstrate effectiveness; 3) speech techniques are a manipulative means, and technical means are used to implement media communication tasks.CONCLUSION. Interactive speech techniques in media texts allow to influence the user, involve him in the news item and form a certain opinion about it, motivate to leave a social reaction to the media content. This gives the authors the opportunity to make the platform more recognizable and interesting, as well as to increase the effectiveness of the community.

In today’s digital age, the majority of people obtain their news via the Internet and social media. However, it is difficult to identify which sources are reliable and which are disseminating false information. So far, many models have been derived to detect fake news or misinformation from social media news using machine learning (ML) or deep learning (DL) techniques. This chapter aims to enhance the detection accuracy by representing the text of social media news in a hybrid format using Natural Language Processing (NLP) techniques. We model the input data of news text in a hybrid representation using TF-IDF with N-grams model combined with Latent Semantic Indexing. The main objective of the hybrid text representation is to represent news text by considering the three important factors of text, viz. the important words in the text, their sequence of occurrence, and their semantic meaning. We applied different ML and DL techniques for news classification and compared the performance of fake news detection models with and without hybrid text representation. The obtained results evidence that there is a significant improvement in detection accuracies when the news text is represented in a hybrid format as proposed in our approach.

PurposeThe purpose of this study is three fold: to provide a preliminary exploration of meeting planners' use and perceived usefulness of the different types of social media; to examine why meeting planners use social media and; to investigate the perception of adopting the social media, especially as perceived critical mass impacts the adoption of social networking media.Design/methodology/approachData were collected from the members of a professional association for meeting professionals in the Southwest US using an online self‐administered questionnaire. A total of 510 members received an invitation to take the survey and 120 responses were received, representing a 23.5 percent response rate. Descriptive analysis, discriminant validity, reliability and path analysis were used to estimate the relationships between the five constructs: perceived critical mass, usefulness, ease of use, attitudes and intention to use social network media in the future.FindingsThe most commonly preferred social network sites were Facebook (29 percent), LinkedIn (15 percent), YouTube (13 percent), Twitters (11 percent) and My Space (11 percent) and the social networking media rated most useful were Facebook (mean=3.7), LinkedIn (mean=3.1), YouTube (mean=3.0), Blogs (mean=2.7), Webinars (mean=2.6) and Twitter (mean=2.5), The top three reasons for using social media were: to communicate with other planners easily and quickly through chat or discussion boards (80.4 percent), to share queries, problems, solutions and opinions with other meeting planners (70.1 percent) and to get feedback from attendees after meeting/event/convention (69.9 percent). Additionally, the path model used in the analysis indicated that perceived critical mass not only directly influences intention to use social network media but also indirectly affects attitude toward using social media and intention to use social media simultaneously through perceived ease of use and perceived usefulness.Originality/valueEven though the social networking media has previously been used by many meeting planners to find information, few research studies have explored the meeting planners' perception of social networking media and what factors may have an effect on meeting planners' adoption of using social network media. This study provides a preliminary empirical analysis of meeting planners' perception of these tools and the factors that influence their utilization.

Negative Secular Trends in Medicine: Part VIII: Practicing Physicians Posting on Social Media

BACKGROUND Depression affects more than 350 million people globally. Traditional diagnostic methods have limitations. Analyzing textual data from social media provides new insights into predicting depression using machine learning. However, there is a lack of comprehensive reviews in this area, which necessitates further research. OBJECTIVE This review aims to assess the effectiveness of user-generated social media texts in predicting depression and evaluate the influence of demographic, language, social media activity, and temporal features on predicting depression on social media texts through machine learning. METHODS We searched studies from 11 databases (CINHAL [through EBSCOhost], PubMed, Scopus, Ovid MEDLINE, Embase, PubPsych, Cochrane Library, Web of Science, ProQuest, IEEE Explore, and ACM digital library) from January 2008 to August 2023. We included studies that used social media texts, machine learning, and reported area under the curve, Pearson &lt;i&gt;r&lt;/i&gt;, and specificity and sensitivity (or data used for their calculation) to predict depression. Protocol papers and studies not written in English were excluded. We extracted study characteristics, population characteristics, outcome measures, and prediction factors from each study. A random effects model was used to extract the effect sizes with 95% CIs. Study heterogeneity was evaluated using forest plots and &lt;i&gt;P&lt;/i&gt; values in the Cochran &lt;i&gt;Q&lt;/i&gt; test. Moderator analysis was performed to identify the sources of heterogeneity. RESULTS A total of 36 studies were included. We observed a significant overall correlation between social media texts and depression, with a large effect size (&lt;i&gt;r&lt;/i&gt;=0.630, 95% CI 0.565-0.686). We noted the same correlation and large effect size for demographic (largest effect size; &lt;i&gt;r&lt;/i&gt;=0.642, 95% CI 0.489-0.757), social media activity (&lt;i&gt;r&lt;/i&gt;=0.552, 95% CI 0.418-0.663), language (&lt;i&gt;r&lt;/i&gt;=0.545, 95% CI 0.441-0.649), and temporal features (&lt;i&gt;r&lt;/i&gt;=0.531, 95% CI 0.320-0.693). The social media platform type (public or private; &lt;i&gt;P&lt;/i&gt;&amp;lt;.001), machine learning approach (shallow or deep; &lt;i&gt;P&lt;/i&gt;=.048), and use of outcome measures (yes or no; &lt;i&gt;P&lt;/i&gt;&amp;lt;.001) were significant moderators. Sensitivity analysis revealed no change in the results, indicating result stability. The Begg-Mazumdar rank correlation (Kendall τ&lt;sub&gt;b&lt;/sub&gt;=0.22063; &lt;i&gt;P&lt;/i&gt;=.058) and the Egger test (2-tailed &lt;i&gt;t&lt;sub&gt;34&lt;/sub&gt;&lt;/i&gt;=1.28696; &lt;i&gt;P&lt;/i&gt;=.207) confirmed the absence of publication bias. CONCLUSIONS Social media textual content can be a useful tool for predicting depression. Demographics, language, social media activity, and temporal features should be considered to maximize the accuracy of depression prediction models. Additionally, the effects of social media platform type, machine learning approach, and use of outcome measures in depression prediction models need attention. Analyzing social media texts for depression prediction is challenging, and findings may not apply to a broader population. Nevertheless, our findings offer valuable insights for future research. CLINICALTRIAL PROSPERO CRD42023427707; https://www.crd.york.ac.uk/PROSPERO/view/CRD42023427707

Depression affects more than 350 million people globally. Traditional diagnostic methods have limitations. Analyzing textual data from social media provides new insights into predicting depression using machine learning. However, there is a lack of comprehensive reviews in this area, which necessitates further research. This review aims to assess the effectiveness of user-generated social media texts in predicting depression and evaluate the influence of demographic, language, social media activity, and temporal features on predicting depression on social media texts through machine learning. We searched studies from 11 databases (CINHAL [through EBSCOhost], PubMed, Scopus, Ovid MEDLINE, Embase, PubPsych, Cochrane Library, Web of Science, ProQuest, IEEE Explore, and ACM digital library) from January 2008 to August 2023. We included studies that used social media texts, machine learning, and reported area under the curve, Pearson r, and specificity and sensitivity (or data used for their calculation) to predict depression. Protocol papers and studies not written in English were excluded. We extracted study characteristics, population characteristics, outcome measures, and prediction factors from each study. A random effectsmodel was used to extract the effect sizes with 95% CIs. Study heterogeneity was evaluated using forest plots and P values in the Cochran Q test. Moderator analysis was performed to identify the sources of heterogeneity. A total of 36 studies were included. We observed a significant overall correlation between social media texts and depression, with a large effect size (r=0.630, 95% CI 0.565-0.686). We noted the same correlation and large effect size for demographic (largest effect size; r=0.642, 95% CI 0.489-0.757), social media activity (r=0.552, 95% CI 0.418-0.663), language (r=0.545, 95% CI 0.441-0.649), and temporal features (r=0.531, 95% CI 0.320-0.693). The social media platform type (public or private; P<.001), machine learning approach (shallow or deep; P=.048), and use of outcome measures (yes or no; P<.001) were significant moderators. Sensitivity analysis revealed no change in the results, indicating result stability. The Begg-Mazumdar rank correlation (Kendall τb=0.22063; P=.058) and the Egger test (2-tailed t34=1.28696; P=.207) confirmed the absence of publication bias. Social media textual content can be a useful tool for predicting depression. Demographics, language, social media activity, and temporal features should be considered to maximize the accuracy of depression prediction models. Additionally, the effects of social media platform type, machine learning approach, and use of outcome measures in depression prediction models need attention. Analyzing social media texts for depression prediction is challenging, and findings may not apply to a broader population. Nevertheless, our findings offer valuable insights for future research. PROSPERO CRD42023427707; https://www.crd.york.ac.uk/PROSPERO/view/CRD42023427707.

Place meanings and national parks: A rhetorical analysis of social media texts

The rapidly increasing amount and variety of data coming from satellites and other sources is raising new issues such as the management and exploitation of extremely large and complex datasets (Big Data); the main challenge in the Space and Security domain is to improve the capacity to extract in a timely manner operational (i.e. useful and clear) information from a huge amount of heterogeneous data. The talk will describe the work performed in the context of the Big Data Europe project (http://www.big-data-europe.eu/), where in one of its pilots, we investigate the fusion of information extracted from satellite images and user-generated content on social media. In our pilot, we deploy change detection from satellite images and event detection in social media text on the Big Data Europe distributed processing infrastructure and relate changes in land cover with events extracted from geo-located social media and news text. From the software engineering point of view, this pilot allows us to experiment with the integration of diverse analysis tools into modern big data infrastructures; from the security domain's point of view, it allows us to demonstrate how heterogeneous data fused via geo-temporal indexing. The talk will also present the Big Data Europe modular platform that integrates systems from Apache and European projects into a Big Data swiss army knife. Development within Big Data Europe aims to provide a layer for semantically describing and discovering what data and processing is available at a deployment, to maintain data provenance and lineage including rights and obligations regarding derivative data, and to provide a data integration layer.

By appearance of social media, people are coming to be able to transmit information easily on a personal level. However, because users of social media generally spend little time on describing information, low-quality texts are transmitted and it blocks the spread of information. On transmitted texts in social media, commas and linefeeds are inserted incorrectly, and it becomes a factor of low-quality texts. This paper proposes a method for automatically formatting Japanese texts in social media. Our method formats texts by inserting commas and linefeeds appropriately. In our method, the positions where commas and linefeeds should be inserted are decided based on machine learning using morphological information, dependency relation and clause boundary information. An experiment using Japanese spoken language texts has shown the effectiveness of our method.

The impact of social and conventional media on firm equity value: A sentiment analysis approach

Currently, social media accounts are not only owned personally but also have official accounts from companies or government agencies. Official social media from one agency is important both to establish communication to internally and externally as well as a forum for promotion and branding the image of the agency. The village government is currently not left behind to use social media, one of which is Panca Mukti Village, Bengkulu Tengah Regency, Bengkulu Province. In the service activities carried out to local village officials, it was recognized that it was true that the village already had social media accounts (one of which was Instagram), but was still constrained by the availability of human resources who managed it. In the counseling activities, material was given related to the urgency of using official social media, along with challenges and management strategies also accompanied by the practice of applying contemporary journalism in managing content on social media. From the discussion and counseling activities and training carried out, village officials have a stronger motivation in managing their social media and will reorganize related to the selection of resources that will be responsible for managing social media belonging to Panca Mukti Village, Bengkulu Utara Regency, Bengkulu Province.

Solace in Social Media: Women Unite Under COVID-19

In recent years, social media platforms, such as Twitter and Webio, have become popular sources of information on the web. These platforms contain a wealth of valuable information about user opinions, user interests, events and more. People typically use these platforms to discuss different topics, share their opinions about them and engage in question-andanswer sessions. For example, regarding smartphones, users might discuss the main aspects of a smartphone, such as the overall design, battery capacity, screen size and camera. The natural hierarchical structure of those concepts is often hidden in social media. Discovering the hidden structure can helps users understand people’ preference to a certain topic at different levels of granularity, and show the reasons why they prefer this topic. Over the past decade, research on hierarchical topic models has shown considerable progress. However, these studies may not always be directly applicable to social media due to the shortness and the shallow meaning of social media messages.There are three major challenges when dealing with social media texts. Firstly, compared with traditionally long texts, social media texts suffer from sparsity, and this issuemay result in an incomprehensible and incorrect concept hierarchy. Secondly, social media contains useful information such as social opinions and information about users. Most existing methods perform a flat sentiment analysis on each extracted aspects independently, and ignore the concept hierarchy. In fact, we need to make the sentiment analysis finegrained in order to simultaneously extract the aspects and summarise people’ opinions on those discovered aspects. Thirdly, the current models only discover the concept hierarchy ignoring the community structure of users. Maintaining the consistency of user’s interest on several communities according to various topics and sentiment information is a challenging problem.In this thesis, the limitations of the existing work are addressed and effective solutions are proposed. First, in order to discover the hierarchical structure of social media content, a novel approach called the context coherence model (CCM) is proposed. It recursively top down: (1) organizes the concepts discussed by users in social media texts; and (2) identifies the hierarchical relations among concepts. In the CCM, a new measurement called context coherence is introduced that analyses words in social media texts and determines the similarities among them. Then, the hierarchical relationship between words is determined by recursively partitioning the whole corpus into smaller parts according to the similarity results. Finally, a merging operation is performed to find similar words, group them under the same topic and remove duplicated topics. The approach is evaluated on two real-world data sets. The experiments show that the proposed approach can effectively reveal the hidden structure in social media.Opinions are now reflected in social media on a wide range of topics: trends in pop music, fashion, politics, financial markets, natural disaster responses, sales of products and services, etc. For example, companies may want to understand the feelings of consumers towards their products or services at different levels of granularity. Therefore, the problem of hierarchical extraction is extended to consider sentiment analysis. A structured sentiment analysis (SSA) approach is proposed that summarizes users’ feelings towards those concepts discovered in the tree. Given users’ messages, the hierarchical clustering method is proposed to detect the top aspects interest users, based on their messages, and attach users’ attitudes to them. To perform sentiment analysis, a top-down, lexicon-based approach was designed to identify the polarity of top aspects of a topic. Finally, a simple summarization method was developed to answer questions such as: (1) What is the overall popularity of the product or service? (2) Why do people like or dislike the product or service? and (3) What are the most favourable and unfavourable aspects?Third, modelling the interests of users is particularly important and can help organizations to understand and analyse users’ behaviours and locate influential users at different granularity levels using their sentiment information. A probabilistic model, namely,the hierarchical user sentiment/topic model (HUSTM), is proposed to discover the hidden structure of topics and users while performing sentiment analysis in a unified way. In HUSTM, users who share the same topic and opinion are grouped within the same community. In this approach, the entire structure is a tree where each node is decomposed into a topic/sentiment node and a user-sentiment node. The topic/sentiment node is, in turn, a mixed distribution of words, while the user-sentiment node is a mixed distribution of users. To experimentally demonstrate the advantages of the approach, three real-world data sets were used. The results showed that, compared to other state-of-the-art techniques, the HUSTM approach can more successfully capture users’ interests.