Analysis Of Social Media Data Research Articles

Sentiment analysis of social media data based on chaotic coyote optimization algorithm based time weight‐AdaBoost support vector machine approach

SummarySentiment analysis or opinion mining is exploited in business, customer services, and so forth, where people provide their opinions in the form of reviews. However, the people's opinions are in a perplexing form such as, sarcasm, irony, and implied meaning which can cause an impact on sentiment analysis. The only way to analyze these words is through context. Nevertheless, there still exist some issues, to tackle those issues, a lot of research has been conducted by focusing the feature engineering. However, the optimized output has not been acquired yet. Hence, we propose a novel method known as chaotic coyote optimization algorithm (COA) based time weight‐AdaBoost support vector machine (SVM) approach which can be used to attain the precise classifications in context. The proposed time weight‐AdaBoost SVM can be used to circumvent the drift concept issues and can be utilized for the perfect learning of data for further classifications. Further, the class imbalance issues can be overcome by adopting a modified CO algorithm, that is, the chaotic COA. Furthermore, the proposed work performance is analyzed with state‐of‐art works such as DICE, ABCDM, and SVM approaches. The comparative analysis shows that our proposed work classifies the social media content acquired from Twitter more accurately than the other works. Thus our work outperforms all the existing approaches in terms of accuracy, precision, recall, and F1 score.

Classifying patient and professional voice in social media health posts

BackgroundPatient-based analysis of social media is a growing research field with the aim of delivering precision medicine but it requires accurate classification of posts relating to patients’ experiences. We motivate the need for this type of classification as a pre-processing step for further analysis of social media data in the context of related work in this area. In this paper we present experiments for a three-way document classification by patient voice, professional voice or other. We present results for a convolutional neural network classifier trained on English data from two different data sources (Reddit and Twitter) and two domains (cardiovascular and skin diseases).ResultsWe found that document classification by patient voice, professional voice or other can be done consistently manually (0.92 accuracy). Annotators agreed roughly equally for each domain (cardiovascular and skin) but they agreed more when annotating Reddit posts compared to Twitter posts. Best classification performance was obtained when training two separate classifiers for each data source, one for Reddit and one for Twitter posts, when evaluating on in-source test data for both test sets combined with an overall accuracy of 0.95 (and macro-average F1 of 0.92) and an F1-score of 0.95 for patient voice only.ConclusionThe main conclusion resulting from this work is that combining social media data from platforms with different characteristics for training a patient and professional voice classifier does not result in best possible performance. We showed that it is best to train separate models per data source (Reddit and Twitter) instead of a model using the combined training data from both sources. We also found that it is preferable to train separate models per domain (cardiovascular and skin) while showing that the difference to the combined model is only minor (0.01 accuracy). Our highest overall F1-score (0.95) obtained for classifying posts as patient voice is a very good starting point for further analysis of social media data reflecting the experience of patients.

Information Laundering and Counter-Publics: The News Sources of Islamophobic Groups on Twitter

Which news sources do supporters of populist islamophobic groups and their opponents rely on, and how are these sources related to each other? We explore these questions by studying the websites referenced in discussions sur- rounding Pegida, a right-wing populist movement based in Germany that is opposed to what its supporters regard as is- lamization, cultural marginalization and political correct- ness. We draw on a manual content analysis of the news sources and the stances of Twitter users, to then calculate the overlap of sources across audiences. Finally, we perform a cluster analysis of the resulting user groups, based on shared sources. Preferences by language, nationality, region and politics emerge, showing the distinction between differ- ent groups among the users. Our tentative findings have im- plications both for the study of mass media audiences through the lens of social media, and for research on the public sphere and its possible fragmentation in online dis- course. This contribution, which is the result of an interdis- ciplinary collaboration between communication scholars in Germany and journalists in Austria, is part of a larger ongo- ing effort to understand forms of online extremism through the analysis of social media data.

A sentiment analysis-based expert weight determination method for large-scale group decision-making driven by social media data

In the big data era, it is practical and generalizable to use social media data for decision-making. Thus, determining how to effectively use this kind of data to support large-scale group decision-making (LSGDM) is a direction worth studying. In this paper, for the first time, the sentiment analysis of social media data is used to evaluate the quality of LSGDM and then to dynamically determine the weights of experts from the statistical perspective. The proposed method helps improve the objectivity, accuracy, and acceptability of LSGDM. A case application demonstrates the feasibility of this method, and the results of comparative analyses reveal its superior accuracy and stability to a certain extent.

Probabilistic Graph Model Mining User Affinity in Social Networks

In social networks, discovery of user similarity is the basis of social media data analysis. It can be applied to user-based product recommendations and inference of user relationship evolution in social networks. In order to effectively describe the complex correlation and uncertainty for social network users, the accuracy of similarity discovery is improved theoretically for massive social network users. Based on the Bayesian network probability map model, network topological structure is combined with the dependency between users, and an effective method is proposed to discover similarity in social network users. To improve the scalability of the proposed method and solve the storage and computation problem of mass data, Bayesian network distributed storage and parallel reasoning algorithm is proposed based on Hadoop platform in this paper. Experimental results verify the efficiency and correctness of the algorithm.

Assessing vulnerability to psychological distress during the COVID-19 pandemic through the analysis of microblogging content

In recent years we have witnessed a growing interest in the analysis of social media data under different perspectives, since these online platforms have become the preferred tool for generating and sharing content across different users organized into virtual communities, based on their common interests, needs, and perceptions. In the current study, by considering a collection of social textual contents related to COVID-19 gathered on the Twitter microblogging platform in the period between August and December 2020, we aimed at evaluating the possible effects of some critical factors related to the pandemic on the mental well-being of the population. In particular, we aimed at investigating potential lexicon identifiers of vulnerability to psychological distress in digital social interactions with respect to distinct COVID-related scenarios, which could be “at risk” from a psychological discomfort point of view. Such scenarios have been associated with peculiar topics discussed on Twitter. For this purpose, two approaches based on a “top-down” and a “bottom-up” strategy were adopted. In the top-down approach, three potential scenarios were initially selected by medical experts, and associated with topics extracted from the Twitter dataset in a hybrid unsupervised-supervised way. On the other hand, in the bottom-up approach, three topics were extracted in a totally unsupervised way capitalizing on a Twitter dataset filtered according to the presence of keywords related to vulnerability to psychological distress, and associated with at-risk scenarios. The identification of such scenarios with both approaches made it possible to capture and analyze the potential psychological vulnerability in critical situations.

The application of artificial intelligence and data integration in COVID-19 studies: a scoping review.

ObjectiveTo summarize how artificial intelligence (AI) is being applied in COVID-19 research and determine whether these AI applications integrated heterogenous data from different sources for modeling.Materials and MethodsWe searched 2 major COVID-19 literature databases, the National Institutes of Health’s LitCovid and the World Health Organization’s COVID-19 database on March 9, 2021. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guideline, 2 reviewers independently reviewed all the articles in 2 rounds of screening.ResultsIn the 794 studies included in the final qualitative analysis, we identified 7 key COVID-19 research areas in which AI was applied, including disease forecasting, medical imaging-based diagnosis and prognosis, early detection and prognosis (non-imaging), drug repurposing and early drug discovery, social media data analysis, genomic, transcriptomic, and proteomic data analysis, and other COVID-19 research topics. We also found that there was a lack of heterogenous data integration in these AI applications.DiscussionRisk factors relevant to COVID-19 outcomes exist in heterogeneous data sources, including electronic health records, surveillance systems, sociodemographic datasets, and many more. However, most AI applications in COVID-19 research adopted a single-sourced approach that could omit important risk factors and thus lead to biased algorithms. Integrating heterogeneous data for modeling will help realize the full potential of AI algorithms, improve precision, and reduce bias.ConclusionThere is a lack of data integration in the AI applications in COVID-19 research and a need for a multilevel AI framework that supports the analysis of heterogeneous data from different sources.

Comparing user-content interactivity and audience diversity across news and satire: differences in online engagement between satire, regular news and partisan news

ABSTRACT Normative theory on the functioning of the public sphere requires citizens to actively engage with the information that is provided to them. For a long time, however, the possibilities of user-content interactivity have been limited due to the one-directionality of the traditional mass media. Moreover, a re-occurring question is to what extent less-versus-more entertaining forms of news evoke audience engagement. This study analyzes the user-content engagement on online platforms in response to journalistic content and infotainment; more concretely, we compare whether the satire genre is more likely to evoke user-content interactivity than regular news and partisan news shows. To test our hypotheses, a large-scale data analysis of social media posts by a wide variety of American TV shows on Facebook, YouTube, and Twitter has been conducted. Results demonstrate satire’s potential to encourage user-content interactivity: Satire videos generated (a) more likes and (b) more comments than the clips of regular news. However, we also find that (c) satire videos are related to less controversy, which arguably indicates that satire hampers the exchange of diverse ideas. Compared to partisan news – which shares many features of satire, but often lacks the humor component – satire elicits more likes but less commentary and less controversy.

Applying natural language processing algorithm for predicting consumer product preferences in retail stores

Technology has changed the way retailers predict and understand consumer behaviour. One such technology that can enable retailers to understand consumer preference is Natural Language Processing (NLP). Social media content including the opinions and interests of the customers is recognized as a valuable source of information for businesses. This study aims to perform a semantic analysis of tweets with the use of an NLP algorithm. This study focuses on building an intelligent application capable of predicting the category of goods a customer would most likely buy in a retail store. This study focuses on analysing social media data with NLP to predict what a customer would buy in a retail store. In this study, we measured a 0.3 increase in accuracy when only various forms of nouns were extracted and analysed. Further research may include Named-Entity Recognition (NER), especially for proper nouns. The researchers believe that this study will contribute to changing the trajectory in which NLP is applied in the retail industry. Therefore, the methodology and design used herein will improve the existing approaches that have already been employed concerning NLP and social media data analysis.

Topic-level sentiment analysis of social media data using deep learning

Due to the inception of Web 2.0 and freedom to facilitate the dissemination of information, sharing views, expressing opinions with regards to current world level events, services, products, etc. social media platforms have been mainly contributing to user-generated content. Such social media data consist of various themes discussed online and are associated with sentiments of the users. To catch up with the speed of streaming data at which it generates on social media platforms, it is crucial to detect the topics being discussed on social media platforms and analyze the sentiments of users towards those topics in an online manner to make timely decisions. Motivated by the same, this paper proposes a deep learning based topic-level sentiment analysis model. The novelty of the proposed approach is that it works at the sentence level to extract the topic using online latent semantic indexing with regularization constraint and then applies topic-level attention mechanism in long short-term memory network to perform sentiment analysis. The proposed model is unique in the sense that it supports scalable and dynamic topic modeling over streaming short text data and performs sentiment analysis at topic-level. For SemEval-2017 Task 4 Subtask B dataset as a case of in-domain topic-level sentiment analysis, average recall of 0.879 has been achieved, whereas, for out-of-domain data, average recall of 0.846, 0.824 and 0.794 has been achieved for newly developed datasets collected under the hashtags #ethereum, #bitcoin and #facebook from Twitter. To assess the performance of the model for scalability, we analyzed the model in terms of average time in milliseconds for creation of feature vectors, throughput in terms of topics detected per second and average response time in seconds to handle the sentiment analysis queries. The experimental results are significant enough to enable large scale topic modeling over streaming data and perform topic-level sentiment analysis.

Expanding the toolbox: Assessing methods for local outdoor recreation planning

Growing demand for outdoor recreation puts pressure on highly-frequented peri-urban areas. In the Netherlands, a more equal distribution of recreationists has been proposed by the Dutch government to relieve pressure on overcrowded recreation hotspots. Devising landscape planning strategies that redirect recreation flows requires reliable data on areas that people currently use and what areas would also be suitable for recreation. Such data may not be available for local planners. The objective of this study was therefore to analyse how different data gathering methods can support local recreation planning.For an empirical case study in the Netherlands, we compare data on current and potential use of landscapes for outdoor recreation through participatory mapping, free listing, quantitative photo ranking, and analysis of social media data. Based on maps produced from these different data we interviewed local planners to assess how applicable and relevant the different methods are for planning practice. Local planners indicated they had limited knowledge of and access to scientific methods and insights. Their assessment of different methods suggests that some methods are not yet applied for local planning, such as free listing or participatory mapping. These methods have potential to be used for evidence-based planning, because they require few resources to be implemented. Planners also indicated that using social media data would be interesting, but that the skills required to collect and analyse data are typically not currently in place. Our results show the potential and challenges for research methods to be integrated into local planning practice to distribute recreation flows more effectively.

Quantifying emotional differences in urban green spaces extracted from photos on social networking sites: A study of 34 parks in three cities in northern China

In rapid urbanization processes, the high density and fast rhythm of the urban living environment lead to mental tension, life pressure, anxiety, and other problems for people, which facilitates negative emotions. Studies have confirmed that an urban green space is a “restorative environment" that can effectively alleviate negative emotions and improve moods. Social media data can be objectively quantified to evaluate people's emotional responses to urban green spaces. Based on the analysis of social media data, this study examines whether there are differences in emotions linked to locations, with the data extracted from microblogs similar to Twitter in three different cities. Seven emotions (happiness, fear, surprise, anger, disgust, neutral, and sadness) were identified using online cognitive services on photos collected from microblog data. Three emotion-based indexes (emotion intensity index, emotion intensity index, emotion evenness index) were used to expand the urban green space pair from the spatial perspective of research on the influence of emotion. The results show that there are significant correlations among age, gender, emotional diversity, area, accessibility, NDVI, and emotion. The results also show that (1) the higher the NDVI of urban green space, the greater the probability and intensity of emotion in green space, and the more happiness emotion people show. (2) there is a significant positive correlation between urban green space area and EEI, and a significant negative correlation between urban green space area and EPI, EII, which is just opposite to the NDVI results. (3) Optimistic and positive emotions are widely expressed in cities, and happiness is expressed most and strongly. The negative emotions such as fear, anger, and trouble are less expressed, and the expressions of emotions in cities are highly similar. With the increase of age, happiness emotion increased, and sadness emotion decreased.These results show that the urban green space planning can be used to intervene in the expression of human emotion based on where a person lives. In the future, the planning and design of urban green spaces based on the reasonable balance and combinations of area, accessibility, and the NDVI can enhance the emotional enthusiasm for these areas. Improving the planning and design of the existing urban parks and green spaces with positive intervention methods can provide a feasible solution to meet the growing population's emotional recovery needs.

Emotional Attitudes of Chinese Citizens on Social Distancing During the COVID-19 Outbreak: Analysis of Social Media Data.

BackgroundWuhan, China, the epicenter of the COVID-19 pandemic, imposed citywide lockdown measures on January 23, 2020. Neighboring cities in Hubei Province followed suit with the government enforcing social distancing measures to restrict the spread of the disease throughout the province. Few studies have examined the emotional attitudes of citizens as expressed on social media toward the imposed social distancing measures and the factors that affected their emotions.ObjectiveThe aim of this study was twofold. First, we aimed to detect the emotional attitudes of different groups of users on Sina Weibo toward the social distancing measures imposed by the People’s Government of Hubei Province. Second, the influencing factors of their emotions, as well as the impact of the imposed measures on users’ emotions, was studied.MethodsSina Weibo, one of China’s largest social media platforms, was chosen as the primary data source. The time span of selected data was from January 21, 2020, to March 24, 2020, while analysis was completed in late June 2020. Bi-directional long short-term memory (Bi-LSTM) was used to analyze users’ emotions, while logistic regression analysis was employed to explore the influence of explanatory variables on users’ emotions, such as age and spatial location. Further, the moderating effects of social distancing measures on the relationship between user characteristics and users’ emotions were assessed by observing the interaction effects between the measures and explanatory variables.ResultsBased on the 63,169 comments obtained, we identified six topics of discussion—(1) delaying the resumption of work and school, (2) travel restrictions, (3) traffic restrictions, (4) extending the Lunar New Year holiday, (5) closing public spaces, and (6) community containment. There was no multicollinearity in the data during statistical analysis; the Hosmer-Lemeshow goodness-of-fit was 0.24 (χ28=10.34, P>.24). The main emotions shown by citizens were negative, including anger and fear. Users located in Hubei Province showed the highest amount of negative emotions in Mainland China. There are statistically significant differences in the distribution of emotional polarity between social distancing measures (χ220=19,084.73, P<.001), as well as emotional polarity between genders (χ24=1784.59, P<.001) and emotional polarity between spatial locations (χ24=1659.67, P<.001). Compared with other types of social distancing measures, the measures of delaying the resumption of work and school or travel restrictions mainly had a positive moderating effect on public emotion, while traffic restrictions or community containment had a negative moderating effect on public emotion.ConclusionsFindings provide a reference point for the adoption of epidemic prevention and control measures, and are considered helpful for government agencies to take timely actions to alleviate negative emotions during public health emergencies.

There and Back Again: A Commentary on Social Welfare Policy in the Wake of 2020

There and Back Again: A Commentary on Social Welfare Policy in the Wake of 2020

A Framework for Forecasting Outbreak of Infectious Diseases Based on Climate Variability and Social Media Content

The amount of data has risen significantly over the last few years, due to the popularity of some of the data generation sources like social media, electronic health records, sensors and online shopping sites. Analyzing, processing and storing this data is very prominent since it helps to uncover hidden patterns and unknown correlations. A big data analysis and prediction System is proposed in this context, which combines weather observations, health data and social media content in order to forecast the outbreaks of infectious diseases in a locality. Finding information about the determinants of disease outbreaks are required to reduce its effects on populations. An In-mapper combiner based MapReduce algorithm is used to calculate the mean of daily measurements of various climate parameters like temperature, atmospheric pressure, relative humidity, solar and wind. The climatic parameter that may leads to the outbreak of a disease is identified by finding the correlation between the parameters and disease incidence count. To evaluate how user’s tweeting patterns and sentiments matched with the outbreak of diseases, all tweets containing keywords related to diseases are collected using twitter streaming APIs and are analyzed and processed using Spark framework. The performance of proposed model is improved due to the presence of tweet processing. This indicates that the real-time analysis of social media data can provide more effective result rather than working on the historical data.

Improving Informed Consent for Novel Vaccine Research in a Pediatric Hospital Setting Using a Blended Research-Design Approach.

It is necessary to conduct Clinical Trials in children, including for novel vaccines. Children cannot legally provide valid consent, but can assent to research participation. Informed consent and assent communications are frequently criticized for their lack of comprehensibility and often, researchers do not involve patients in informed consent design. We tested a blended research-design approach to co-design multimedia informed consent prototypes for experimental vaccine studies targeted at the pediatric population. We report details on the methodology utilized, and the insights, ideas, and prototype solutions we generated using social media data analysis, a survey, and workshops. A survey of clinical trial researchers indicated that while the most did not use technology for informed consent, they considered its utilization favorable. Social media analysis enabled researchers to quickly understand where community perspectives were concordant and discordant and build their understanding of the types of topics that they may want to focus on during the design workshops. Participatory design workshops for children and their families reaped insights, ideas, and prototypes for a range of tools including apps and websites. Participants felt that the prototypes were better able to communicate necessary content than the original text document format. We propose using a participatory, mixed-methods approach to design informed consent so that it is better adapted to patients' needs. Such an approach would be helpful in better addressing the needs of different segments of the populations involved in clinical trials. Further evidence should be gained about the impact of this strategy in improving recruitment, decreasing withdrawals and litigations, and improving patient satisfaction during clinical trials.

Long Covid: Online patient narratives, public health communication and vaccine hesitancy.

IntroductionThis study combines quantitative and qualitative analyses of social media data collected through three key stages of the pandemic, to highlight the following: ‘First wave’ (March to May, 2020): negative consequences arising from a disconnect between official health communications, and unofficial Long Covid sufferers’ narratives online.‘Second wave’ (October 2020 to January 2021): closing the ‘gap’ between official health communications and unofficial patient narratives, leading to a better integration between patient voice, research and services.‘Vaccination phase’ (January 2021, early stages of the vaccination programme in the UK): continuing and new emerging concerns.MethodsWe adopted a mixed methods approach involving quantitative and qualitative analyses of 1.38 million posts mentioning long-term symptoms of Covid-19, gathered across social media and news platforms between 1 January 2020 and 1 January 2021, on Twitter, Facebook, Blogs, and Forums. Our inductive thematic analysis was informed by our discourse analysis of words, and sentiment analysis of hashtags and emojis.ResultsResults indicate that the negative impacts arise mostly from conflicting definitions of Covid-19 and fears around the Covid-19 vaccine for Long Covid sufferers. Key areas of concern are: time/duration; symptoms/testing; emotional impact; lack of support and resources.ConclusionsWhilst Covid-19 is a global issue, specific sociocultural, political and economic contexts mean patients experience Long Covid at a localised level, needing appropriate localised responses. This can only happen if we build a knowledge base that begins with the patient, ultimately informing treatment and rehabilitation strategies for Long Covid.

Public Perceptions about COVID-19 Vaccines: Policy Implications from US Spatiotemporal Sentiment Analytics

There exists a compelling need to better understand the temporal dynamics of public sentiment towards COVID-19 vaccines in the US on a national and state-wise level for facilitating appropriate public policy applications. Our analysis of social media data from early February of 2021 and late March of 2021 shows that in spite of overall strength of positive sentiment, and increasing numbers of Americans being fully vaccinated, negative sentiment about COVID-19 vaccines still persists among sections of people who are hesitant towards the vaccine. In this study, we performed sentiment analytics on vaccine tweets, studied changes in public sentiment over time, conducted vaccination sentiment validation using actual vaccination data from the US CDC and Household Pulse Survey (HPS), explored influence of maturity of Twitter user-accounts and generated geographic mapping of sentiments by location of Twitter users. Furthermore, we leverage the emotion polarity based Public Sentiment Scenarios (PSS) framework which was developed for COVID-19 sentiment analytics, to systematically analyze directions for public policy processes to potentially improve the administration of vaccines. Application of the PSS framework provides important time sensitive insights for state and federal government agencies and associated organizations to better implement public policy processes for healthcare management, communication, transparency, motivation and societal operational policies such as social distancing. These insights are expected to contribute to processes that can expedite the vaccination program and move closer to the cherished herd immunity goal.

Multi-Class Sentiment Analysis of Social Media Data with Machine Learning Algorithms

The volume of social media data on the Internet is constantly growing. This has created a substantial research field for data analysts. The diversity of articles, posts, and comments on news websites and social networks astonishes imagination. Nevertheless, most researchers focus on posts on Twitter that have a specific format and length restriction. The majority of them are written in the English language. As relatively few works have paid attention to sentiment analysis in the Russian and Kazakh languages, this article thoroughly analyzes news posts in the Kazakhstan media space. The amassed datasets include texts labeled according to three sentiment classes: positive, negative, and neutral. The datasets are highly imbalanced, with a significant predominance of the positive class. Three resampling techniques (undersampling, oversampling, and synthetic minority oversampling (SMOTE)) are used to resample the datasets to deal with this issue. Subsequently, the texts are vectorized with the TF-IDF metric and classified with seven machine learning (ML) algorithms: naïve Bayes, support vector machine, logistic regression, k-nearest neighbors, decision tree, random forest, and XGBoost. Experimental results reveal that oversampling and SMOTE with logistic regression, decision tree, and random forest achieve the best classification scores. These models are effectively employed in the developed social analytics platform.

Development of Social Media Analytics System for Emergency Event Detection and Crisis Management

Social media platforms have proven to be effective for information gathering during emergency events caused by natural or human-made disasters. Emergency response authorities, law enforcement agencies, and the public can use this information to gain situational awareness and improve disaster response. In case of emergencies, rapid responses are needed to address victims' requests for help. The research community has developed many social media platforms and used them effectively for emergency response and coordination in the past. However, most of the present deployments of platforms in crisis management are not automated, and their operational success largely depends on experts who analyze the information manually and coordinate with relevant humanitarian agencies or law enforcement authorities to initiate emergency response operations. The seamless integration of automatically identifying types of urgent needs from millions of posts and delivery of relevant information to the appropriate agency for timely response has become essential. This research project aims to develop a generalized Information Technology (IT) solution for emergency response and disaster management by integrating social media data as its core component. In this paper, we focused on text analysis techniques which can help the emergency response authorities to filter through the sheer amount of information gathered automatically for supporting their relief efforts. More specifically, we applied state-of-the-art Natural Language Processing (NLP), Machine Learning (ML), and Deep Learning (DL) techniques ranging from unsupervised to supervised learning for an in-depth analysis of social media data for the purpose of extracting real-time information on a critical event to facilitate emergency response in a crisis. As a proof of concept, a case study on the COVID-19 pandemic on the data collected from Twitter is presented, providing evidence that the scientific and operational goals have been achieved. © 2021 Tech Science Press. All rights reserved.