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Social networks provide a powerful reflection of the structure and dynamics of the modern society. One of the promising areas for using the results of social networks content analysis is to reveal the hidden patterns of the social processes development and factors that determine the changing moods in different social groups. In this paper, we propo...
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... and sex distribution of the users' activity is shown at the Fig. 4 and Fig. 5. Age category from 17 to 30 years is the most labile in terms of potential protest activity. Men users are much more active in discussion of topics related to social tension problem regardless of the age group. ...
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The sixtieth anniversary of the socialist system in Russia did not pass without traces for the economy of the Russian Federation, which was nascent in the 1990s. Strict regulation of economic processes by the state in the USSR had a number of positive consequences, but closer to the mid-60s of the 20th century it could not adapt to the realities an...
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... For example, Facebook and Twitter are commonly used in the United States alongside platforms like Reddit and YouTube, which attract a global yet often English-speaking audience [17,18]. In contrast, VKontakte (VK) is more popular in Russia [19]. In China, where many Western platforms are restricted, local services like Weibo and WeChat are most popular [20]. ...
Fake news has become a significant challenge on online social platforms, increasing uncertainty and unwanted tension in society. The negative impact of fake news on political processes, public health, and social harmony underscores the urgency of developing more effective detection systems. Existing methods for fake news detection often focus solely on one platform, potentially missing important clues that arise from multiple platforms. Another important consideration is that the domain of fake news changes rapidly, making cross-domain analysis more difficult than in-domain analysis. To address both of these limitations, our method takes evidence from multiple social media platforms, enhances our cross-domain analysis, and improves overall detection accuracy. Our method employs the Dempster–Shafer combination rule for aggregating probabilities for comments being fake from two different social media platforms. Instead of directly using the comments as features, our approach improves fake news detection by examining the relationships and calculating correlations among comments from different platforms. This provides a more comprehensive view of how fake news spreads and how users respond to it. Most importantly, our study reveals that true news is typically rich in content, while fake news tends to generate a vast thread of comments. Therefore, we propose a combined method that merges content- and comment-based approaches, allowing our model to identify fake news with greater accuracy and showing an overall improvement of 7% over previous methods.
... Here, emphasis was placed on a multi-tiered approach to spreading information beginning from government announcements, official reports, and mass media campaigns, and reinforced and further spread through semiformal channels such as community workshops, school programs, and local organizations, as well as informal networks, including social media. Social media can reveal the hidden patterns of social processes and determinants of the changing mood of groups (Ksiazek et al., 2016;Donchenko et al., 2017;Jiang et al., 2021). Waste management authorities can also partner with influencers or grant them ambassadorial roles to create content related to MSWM to foster trust and patronage (Syed et al., 2023;Zhao et al., 2024). ...
This study examined the role of waste management authorities in promoting public awareness of municipal solid
waste management (MSWM) through the lens of the expansion diffusion theory (EDT). EDT emphasizes the
spread of new ideas and practices within a society through various communication channels and distinct individuals
within each system. We employed a mixed-method approach using 116 survey responses from Lagos
residents and five semi-structured in-depth interviews. Our findings reveal the need for a more structured
approach to create public awareness of MSWM, considering the distinct groups of residents in Lagos and their
responses to innovation and knowledge diffusion. We propose four pillars on which waste management authorities
in developing countries can sustain their MSWM awareness campaigns, as well as an awareness
campaign strategy flowchart. Our findings add to the expanding body of research on public awareness and
participation in MSWM, emphasizing the critical role that waste management authorities can play in fostering
sustainable waste management awareness and practices.
... Distinctive machine learning models are yet the most widely recognized utilization. Naïve Bayes is practical when applied on very much shaped content corpus [7] while upholding vector machine it gives a decent execution for low shape dataset. All things considered, the machine learning strategy performs inadequately on Facebook with individuals posting in arbitrary lengths and heaps of spelling errors. ...
Research on crimes committed on social media platforms is
growing fast as more platforms are made available to the general public
(Facebook, Instagram, Twitter, etc.). The focus of this study is on offenses involving verbal abuse directed at a specific individual or group
of individuals. As social media platforms continue to develop at an astronomical pace, users’ mental health is being adversely affected as a
result. Analysis of public comments is the primary focus of the research,
which aims to identify and remove any offensive or improper comments
from them. The findings of this study provide a possible solution to
this particular issue. We evaluate approximately 13 research papers on
verbal harassment comments on social media. In this paper’s review,
we attempt to identify and analyze the concealed factors. We also attempt to introduce some tools and algorithms that are used to identify
harassment-related comments. We also conducted a survey; the survey
questions represent the percentage of social media users who face verbal
harassment. In future work, we will attempt to implement LSTM-based
comment detectors for identifying instances of online harassment. Also,
we provide some justifications for selecting the LSTM algorithm as our
future implementation technique.
... In addition, the comments are used to monitor and make operational decisions on COVID-19 and its consequences [49,50]. But most researchers in the analysis of social media content concentrate mainly on assessing the emotional background of posts and comments [51,53], without attempting to extract the argumentation of users' positions. If we compare the number of studies on the thematic analysis of stances and the thematic analysis of arguments, the latter clearly lose out to the former (see, e.g., the analysis of stances on abortion [5, 16-19, 21, 54] and analysis of arguments on abortion [16]). ...
We propose to consider our experience in data use of Russian-language texts of social networks, electronic media, and search engines in demographic analysis. Experiments on the automatic classification of opinions have been carried out. Conversational RuBERT has been used in most cases. The following main scientific results on text data will be described: (1) short-term forecasts of fertility dynamics according to Google trend data, (2) automatic measurement of the demographic temperature of various demographic groups (pronatalists and antinatalists) in social networks, (3) sentiment analysis of reproductive behavior, sentiment analysis of vital behavior in pandemic, sentiment analysis of attitudes toward demographic and epidemiological policy according to social network data, (4) analysis of the arguments of social network users, and (5) analysis of media publications on demographic policy. A description of the created open databases of all these studies will be provided. All of the studies described will contain reflections on the advantages and difficulties of using texts as data in demographic analysis.
... Опис систем, у яких використовуються методи машинного навчання та сентимент-аналізу і які призначені для аналізу даних, пов'язаних з емоційністю та СН, подано у [14][15][16][17][18][19][20][21][22][23]. У більшості з них коротко або більш розлого подається опис попередньої обробки текстових даних, що включає у себе очистку тексту (видалення стоп-слів, знаків пунктуації, спеціальних символів, емодзі, гештеґів тощо), токенізацію тексту, побудову вкладань слів та інші методики. ...
Introduction. The most important factors causing an increase in the level of tension in society are crisis phenomena and information operations. Today, sociological surveys are considered the main way to determine the level of tension that arises in some group of people in connection with a certain event. However, this method does not allow obtaining detailed information about the dynamics of changes in tension associated with certain news events and the impact of these news events on the general level of tension in society, which complicates the decision-making process by government officials in crisis situations.
The purpose of the work is to increase the situational awareness of representatives of state institutions regarding the current level of social tension provoked by crisis phenomena, news events or information operations. The information obtained will help government officials to make quick decisions to overcome these crisis phenomena and counter disinformation.
The main task of the research is to develop the architecture and mathematical support of the system of operative determination of the level of tension in society based on data from social networks.
Results. The architecture and mathematical support of the system of operative determination of the level of tension in society were developed. An example of the application of this system to determine the level of tension provoked by one news publication is demonstrated. The main advantages and disadvantages of the developed system, as well as directions for further research, are determined.
Conclusions. The developed system of operative determination of the level of tension in society helps to quickly identify news events and news publications that have the greatest impact on increasing the level of social tension across the country at certain specific moments of time. The use of a system based on social networks makes it possible to build on the basis of current data such assessments, which can be used to study the dynamics of changes in social tension associated with a certain news event or news publication.
... Other researchers primarily concentrate on assessing the emotional background of posts (Donchenko et al. 2017) and comments (Sidorov and Slastnikov 2021;Smetanin and Komarov 2021), without specifically extracting the arguments underlying users' positions. ...
Big data provides researchers with valuable sources of information for studying demographic behavior in the population. One such source is the texts posted by social network users on various demographic issues. This study utilizes methods for automatically extracting user opinions from the “VKontakte” social network. The extracted texts are then classified using the Conversational RuBERT neural network model to investigate opinions related to reproductive behavior in the population. The classification process addresses two consecutive problems. Firstly, it aims to identify whether a user’s comment contains argumentation. Secondly, if an argument is present, it seeks to determine its type within the context of the “personal-public” dichotomy. To search for arguments and classify their types, six experiments were conducted, varying the dataset and the number of classes. The method employed for automatic extraction and classification of user opinions on the “VKontakte” social network has demonstrated the ability to accurately classify users’ comments, identifying the presence of argumentation and categorizing the arguments within the “personal-public” dichotomy. This enables the identification of personal and social attitudes, values, stories, and opinions, thus facilitating the study of reproductive behavior.
... The mechanism of expert searching in small social groups can be considered as tools for determining the key tasks of urban development. The technologies for analyzing the links and activity in social networks can be implemented to check the relevance and promote urban projects [20]. This knowledge can offer arguments that are understandable and reflect the interests of each target audience group [21]. ...
... An approach to finding dependency that shows how influential users of social networks can influence the attitude of users to a particular problem is developed to solve the second problem. There is an approach to predict the attitude of users to a particular post using real-time sentiment analysis and clustering methods [20]. This approach is combined with other approaches to identify influencers in the social graph to determine how the tone of the discussion changes. ...
The study of the problem of sustainable development of urban areas led to the understanding of the important role that population involvement plays in solving the issues related to forming a comfortable urban environment. It is normal practice to take into account the proposals and recommendations of citizens and organizations when making decisions in the field of territorial development. Nevertheless, the implementation of participatory governance mechanisms faces significant difficulties. This is primarily due to the lack of effective tools for analyzing the opinion of citizens and technologies for integrating urban communities into existing institutions of governance. This article discusses the ways of organizing expert polls based on the principles of “Socratic dialogue”. The idea of using social networks for testing and promoting urban development projects by analyzing social reactions to various urban initiatives is considered. The possibility of predicting public opinion about a specific solution for urban development using historical data from social networks has been investigated. An approach to the gamification of work with a social response to the urban environment state through virtual interactions with infrastructure objects is proposed. The prospects for gamification to motivate civic participation and improve the efficiency of feedback mechanisms between the city and its residents are discussed.
... In addition to public manifestations of common social moods -strikes, protests, riots, aimed at demonstrating a clear negative attitude towards certain social issues, social media have become a powerful tool of influence on people, their minds and behaviors. Articles, blogs, forums, social networks provide the massive textual data to discover useful patterns on human emotions, behaviors and sentiments expressed to certain events [6]. ...
The media are considered to be a pivotal source of creating and reflecting various processes in modern societies. The media’s role in escalating social tension in the USA is essential and will be examined in detail in the article. The work focuses on the language used in the media to create social tension, the phenomenon, caused by worried feelings, no trust, stress, hostility, unfriendliness, not sharing common needs or interests, anxiety, violence, and conflict. The pandemic of COVID-19 has led to inequality of views in terms of health and life-activities and has triggered off a wave of anxiety, stress and other manifestations of tension in different social spheres. The analysis of the mass media, namely publicist articles, blogs, forums, and posts in social networks, allows seeing people’s discontent about the current realities in medicine, education, ecology, and politics. People’s feelings of discontent and stress can be generally conceptualized as social tension.
... In our recent survey [19], we identified a significant amount of applied studies that analysed different complex types of discourse in Russian-speaking social media, e.g. attitudes to ethnic groups and migrants [20]- [23], the Ukrainian crisis [24]- [26], and social tension [27], [28]. Researchers primarily examined user-generated content from social network sites, using a combination of topic modelling and sentiment analysis techniques. ...
With the widespread use of online social networks, it is becoming more and more difficult to monitor and analyse all the user-generated content. Toxic speech in online conversations should be treated as a matter with serious social gravity, since it may result in both negative impacts on mental health and violent actions in the physical world. Within this study, we identified the share of toxic comments among different topics in Russian-language comments from social network Pikabu. Firstly, for toxic comments classification, we manually labelled the training dataset and fine-tuned several language models. To provide further toxic comments studies with strong classification baselines, we made our pre-trained publicly available to the research community. Secondly, we proposed an approach for topics labelling based on six major objective and observable dimensions for objective wellbeing measurement used by intergovernmental and government organisations. Lastly, we conducted an analysis of Pikabu data. We found that the largest share of toxic comments was under posts about politics, while security and socioeconomic topics ranked second and third, and the rest of the topics showed roughly the same values.
... The analyses of user engagement in social media can reveal latent patterns and discover factors that evaluate managerial effectiveness (Donchenko et al., 2017). User engagement in social media includes showing 'likes' (Ding et al., 2017), sharing experiences (Alinejad, 2019), making comments (Gaenssle and Budzinski, 2021) and interacting with other users (Hu et al., 2017). ...
Waste segregation, recycling and reduction have been prioritised in the Circular Economy transition of household waste management to reduce environmental impacts. With digitalisation and innovation developments in waste management, residents become more active on waste management-related social media platforms. However, it is still needed a tangible analysis of resident engagement (e.g. user comments and interactions) and related sentiment changes on such platforms to enhance waste management and ease the environmental burden at source. This study develops an integrated solution to analyse resident engagement by leveraging statistical analysis and text-mining methods. Four interrelated components are incorporated in the solution: population behaviour quantification, sentiment analysis and dynamics, popular concerns and probability distribution fitting, and rule-based managerial insight identification. The novel solution is applied to a real-world case study on a subscription account related to waste management in Shanghai. This research produces several major observations based on the studied case: (i) The resident engagement Monday-to-Thursday was more active than Friday-to-Sunday. (ii) Compared to 2018, the resident engagement by commenting on online posts was elevated by 107.1% in 2019 when Shanghai introduced a new management policy. Meanwhile, the yearly resource-type waste collection was increased by 114.5% in 2019. (iii) It took approximately one year to recover positive sentiments in user comments after introducing the policy. However, the comments with negative sentiments assisted in improving waste management. (iv) The best-fitted negative binomial distribution of the number of votes for user comments could guarantee the managerial insight identification from the minority of comments with popular concerns.
