Analysis Of Social Media Data Research Articles

Recurrent neural network based sentiment analysis of social media data during corona pandemic under national lockdown

The Corona virus pandemic has affected the normal course of life. People all over the world take the social media to express their opinions and general emotions regarding this phenomenon. In a relatively short period of time, tweets about the new Corona virus increased by an amount never before seen on the social networking site Twitter. In this research work, Sentiment Analysis of Social Media Data to Identify the Feelings of Indians during Corona Pandemic under National Lockdown using recurrent neural network is proposed. The proposed method is analyzed using four steps: that is Data collection, data preparation, Building sentiment analysis model and Visualization of the results. For Data collection, the twitter dataset are collected from social networking platform twitter by application programming interface. For Data preparation, the input data set are pre-processed for removing URL links, removing unnecessary spaces, removing punctuations and numbers. After data cleaning or preprocessing entire particular characters and non-US characters from Standard Code for Information Interchange, apart from hash tag, are extracted as refined tweet text. In addition, entire behaviors less than three alphabets are not assumed at analysis of tweets, lastly, tokenization and derivation was carried out by Porter Stemmer to perform opinion mining. To authenticate the method, categorized the tweets linked to COVID-19 national lockdown. For categorization, recurrent neural method is used. RNN classify the sentiment classification as positive, negative and neutral sentiment scores. The efficiency of the proposed RNN based Sentimental analysis classification of COVID-19 is assessed various performances by evaluation metrics, like sensitivity, precision, recall, f-measure, specificity and accuracy. The proposed method attains 24.51%, 25.35%, 31.45% and 24.53% high accuracy, 43.51%, 52.35%, 21.45% and 28.53% high sensitivity than the existing methods.

Do we need to mine social media data to detect exotic vertebrate-pest introductions?

Invasive alien species are responsible for considerable biodiversity loss and environmental damage. Timely detection of new incursions is critical in preventing novel populations establishing. Citizen reports currently account for the majority of alien species detections, arising from the massive observation effort that the physical and digital ‘eyes and ears’ of citizens provide, in combination with crowd-sourced species identification. Because the reporting of alien species sightings is generally not mandatory, there is interest in whether mining social media data via image recognition and/or natural language processing can improve on existing passive citizen surveillance in a cost-effective manner. Here, we illustrate, using examples from Australia, how citizen surveillance for most vertebrate groups appears to currently be effective using existing voluntary reporting mechanisms. Where citizen surveillance is currently ineffective, for reasons of inadequate sampling, data mining of social media feeds will be similarly affected. We argue that mining citizens’ social media data for evidence of invasive alien species needs to demonstrate not only that it will be an improvement on the business as usual case, but also that any gains achieved cannot be achieved by alternative approaches. We highlight the potential role of education in increasing the surveillance effectiveness of citizens for detecting and reporting sightings of alien species. Should data mining of social media platforms be pursued, we note that the scale of the task in terms of the potential number of exotic vertebrate species to be classified is very large. The expected number of false positive classifications would present a considerable workload to process, possibly undermining the efficiency rationale for the use of data mining. Hence, prioritisation is needed, and we illustrate how the number of species to be classified can be reduced considerably. If we are to deploy data mining and analysis of social media data to help with detecting introductions of invasive alien species, we need to conduct it in a manner where it adds value and is trusted.

Machine Learning Techniques for Disease Prediction

Epidemic disease prediction is a critical area of research that has garnered increasing attention in recent years, particularly in the wake of the COVID-19 pandemic. One promising avenue for predicting the spread of diseases is through the analysis of social media data, such as Twitter. Machine learning (ML) techniques can be applied to Twitter data to identify patterns and trends that may be indicative of an emerging epidemic. For example, natural language processing (NLP) techniques can be used to analyze the language used in tweets to identify keywords and phrases that are commonly associated with a particular disease. Additionally, sentiment analysis can be used to assess the overall mood of the Twitter community, which can be a useful predictor of disease outbreaks. By combining these techniques with real-world data on disease incidence and other relevant factors, it may be possible to develop highly accurate models for predicting the spread of epidemic diseases, which could have important implications for public health policy and emergency response planning.

Integration of deep learning techniques for sentiment and emotion analysis of social media data

Integration of deep learning techniques for sentiment and emotion analysis of social media data

Integration of deep learning techniques for sentiment and emotion analysis of social media data

Integration of deep learning techniques for sentiment and emotion analysis of social media data

Public Perception Before and After COVID-19 Vaccine Pass for the Unvaccinated to Eat Alone: Social Media Data Analytics.

This study was conducted to compare the trends of public perception in South Korea about the vaccine pass requiring the unvaccinated to eat alone during the COVID-19 crisis. Data were collected via Text mining; frequency, association, and sentiment analysis using the social big data analysis service, (known as "Some-Trend"), 2 months before and after December 16, 2021, when the vaccine pass was announced. The total number of search results was 4899 occurrences of the keywords using "eating alone" and "Hon-bab" (Korean abbreviation for eating alone). During the week of December 16, the frequency was the highest (770 occurrences). Compared to the weeks before the announcement sentiment analysis shows that words including "Reject," "Discrimination," and "Uncomfortable," among others, either newly appeared or increased in frequency. And also, the percentage of positive words decreased from 54.5% to 34% and that of negative words increased from 30.2% to 43.3%. The introduction of the vaccine pass has raised negative public interest, particularly regarding the policy of unvaccinated people forcefully restricted to eat alone. Accordingly, this study showed that the vaccine policy had not gain positive perception of the public.

The Analytical Tools for Tourism Development through Social Media Data and Spatial Morphological Analysis

In the tourism sector, social media data help elucidate the travel behavior and activities of travelers through images and reviews. However, geospatial studies with spatial analysis tools such as GIS are still required by researchers who hope to gain beneficial insight into tourism development from such social media data. In addition to spatial studies, Space syntax analysis is used to explain the spatial characteristics and accessibility of both pedestrians and vehicles; however, the method doesn't consider how people use the space. Therefore, using social media with space syntax analysis will help enhance the understanding of both tourism and spatial aspects. This research aims to develop analytical tools to support tourism studies with the GIS (graphic information system) process using social media data analysis and spatial morphological results from space syntax analysis. The study found that the accessibility potential of a destination does not enhance the popularity of tourist attractions as much as relevant images or attractions due to many popular tourist attractions being located in areas with low potential accessibility. While the image processing and text processing results can extract hidden traits and identities in each attraction, another result is that the analytical tool developed in the study can help quantify tourism activities and perform preliminary content analysis of the data from text and Image processing through the GIS software. It is also used to overlay tourism activities and spatial morphology results in order to recognize the tourism development potential of the area in terms of destination image tourism activities and accessibilities. The tool produces useful results and assists in decision-making for the development of tourist attractions.

A Large-Scale Observational Analysis of Social Media Data Reveals Major Public Misperception of the Attainability of Drastic Weight Loss by Dieting

Introduction: Diet forums in social media websites provide an opportunity to glimpse the experience of different weight loss diet strategies reported by tens of thousands of individuals. Methods: We analyzed all postings with weight information from the six major Reddit weight loss diet forums (“subreddits”) as reported by forum participants. Results: Data were collected from January 2011 to April 2020 from all 55,900 users posting weight information. Average start BMI was in the overweight or obese range (26–34 kg/m²), and average goal BMI was in the normal range (21.5–24.5 kg/m²) for all subreddits. There is correlation between start BMI and goal BMI (R² = 0.63, p < 10⁻¹⁰) and between planned weight loss and reported weight loss (R² = 0.56, p < 10⁻¹⁰). Approximately 80% of forum participants reported a weight loss that was greater than 5% of their initial body weight. Actual reported weight loss was less than half of goal weight loss. Average reported weight loss and adherence were highest in the keto and loseit subreddits. More upvotes and fewer downvotes were associated with higher reported weight loss in five of the six subreddits. Conclusions: Despite the need for cautious interpretation of these data due to self-selection of users who updated weight loss and the possibility of unreliable weight reports, the study has several findings. Average goal BMI was in the normal weight range, demonstrating a highly unrealistic perception, in a very large lay-public cohort, of the plausibility of losing all excess weight. The success in weight loss and maintenance in self-selected individuals who continued reporting weight for many months may demonstrate the subjective value some individuals can obtain from forum participation.

Vaccine Vigilance System: Considerations on the Effectiveness of Vigilance Data Use in COVID-19 Vaccination.

(1) Background: The safety of medicines has been receiving increased attention to ensure that the risks of taking medicines do not outweigh the benefits. This is the reason why, over several decades, the pharmacovigilance system has been developed. The post-authorization pharmacovigilance system is based on reports from healthcare professionals and patients on observed adverse reactions. The reports are collected in databases and progressively evaluated. However, there are emerging concerns about the effectiveness of the established passive pharmacovigilance system in accelerating circumstances, such as the COVID-19 pandemic, when billions of doses of new vaccines were administered without a long history of use. Currently, health professionals receive fragmented new information on the safety of medicines from competent authorities after a lengthy evaluation process. Simultaneously, in the context of accelerated mass vaccination, health professionals need to have access to operational information-at least on organ systems at higher risk. Therefore, the aim of this study was to perform a primary data analysis of publicly available data on suspected COVID-19 vaccine-related adverse reactions in Europe, in order to identify the predominant groups of reported medical conditions after vaccination and their association with vaccine groups, as well as to evaluate the data accessibility on specific syndromes. (2) Methods: To achieve the objectives, the data publicly available in the EudraVigilance European Database for Suspected Adverse Drug Reaction Reports were analyzed. The following tasks were defined to: (1) Identify the predominant groups of medical conditions mentioned in adverse reaction reports; (2) determine the relative frequency of reports within vaccine groups; (3) assess the feasibility of obtaining information on a possibly associated syndrome-myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS). (3) Results: The data obtained demonstrate that the predominant medical conditions induced after vaccination are relevant to the following categories: (1) "General disorders and administration site conditions", (2) "nervous system disorders", and (3) "musculoskeletal and connective tissue disorders". There are more reports for mRNA vaccines, but the relative frequency of reports per dose administered, is lower for this group of vaccines. Information on ME/CFS was not available, but reports of "chronic fatigue syndrome" are included in the database and accessible for primary analysis. (4) Conclusions: The information obtained on the predominantly reported medical conditions and the relevant vaccine groups may be useful for health professionals, patients, researchers, and medicine manufacturers. Policymakers could benefit from reflecting on the design of an active pharmacovigilance model, making full use of modern information technologies, including big data analysis of social media and networks for the detection of primary signals and building an early warning system.

Populist platform strategies: a comparative study of social media campaigning by Nordic right-wing populist parties

ABSTRACT Social media platforms are increasingly becoming an important tool to mobilize populist right-wing issues and movements. This study provides comparative insights into the activity and engagement of right-wing populist parties on three social media platforms (Facebook, Instagram, and Twitter) in four Nordic countries (Sweden, Denmark, Norway, and Finland). Based on a quantitative analysis of social media data, we conclude that Facebook is the most successful platform for right-wing populists across all four countries and that the right-wing populists in Sweden have the strongest position across platforms. Furthermore, we explore the content of the most engaging status updates qualitatively to identify a potential set of populist platform strategies. We conclude that the right-wing populist platform strategies are not radically different from other parties though the populist agenda and anger-based style of communication may cater particularly well to the network media logics of each platform. This could explain the relative success of right-wing populist parties that we identify in all four Nordic countries, even though it is important to note that the success is only moderate in some cases with notable national variation.

Increasing the Investment’s Opportunities in Kingdom of Saudi Arabia By Studying and Analyzing the Social Media Data

Social networking sites are a significant source of information to know the behavior of users and to know what is occupying society of all ages and accordingly helpful information can be provided to specialists and decision-makers. According to official sources, 98.43% of Saudi youth use social networking sites. The study and analysis of social media data are done to provide the necessary information to increase investment opportunities within the Kingdom of Saudi Arabia, by studying and analyzing what people occupy on the communication sites through their tweets about the labor market and investment. Given the huge volume of data and also its randomness, a survey of the data will be done and collected from through keywords, the priority of arranging the data, and recording it as (positive - negative - mixed). The study analysis and conclusion will be based on data-mining and its techniques of analysis and deduction.

Sentiment analysis on social media tweets using dimensionality reduction and natural language processing

AbstractSocial media has been embraced by different people as a convenient and official medium of communication. People write or share messages and attach images and videos on Twitter, Facebook and other social media platforms. It therefore generates a lot of data that is rich in sentiments. Sentiment analysis has been used to determine the opinions of clients, for instance, relating to a particular product or company. Lexicon and machine learning approaches are the strategies that have been used to analyze these sentiments. The performance of sentiment analysis is, however, distorted by noise, the curse of dimensionality, the data domains and the size of data used for training and testing. This article aims at developing a model for sentiment analysis of social media data in which dimensionality reduction and natural language processing with part of speech tagging are incorporated. The model is tested using Naïve Bayes, support vector machine, and K‐nearest neighbor algorithms, and its performance compared with that of two other sentiment analysis models. Experimental results show that the model improves sentiment analysis performance using machine learning techniques.

Machine learning and Natural Language Processing of social media data for event detection in smart cities

Social media data analysis in a smart city context can represent an efficacious instrument to inform decision making. The manuscript strives to leverage the power of Natural Language Processing (NLP) techniques applied to Twitter messages using supervised learning to achieve real-time automated event detection in smart cities. A semantic-based taxonomy of risks is devised to discover and analyse associated events from data streams, with a view to: (i) read and process, in real-time, published texts (ii) classify each text into one representative real-world category (iii) assign a citizen satisfaction value to each event. To select the language processing models striking the best balance between accuracy and processing speed, we conducted a pre-emptive evaluation, comparing several baseline language models formerly employed by researchers for event classification. A heuristic analysis of several smart cities and community initiatives was conducted, with a view to define real-world scenarios as basis for determining correlations between two or more co-occurring event types and their associated levels of citizen satisfaction, while further considering environmental factors. Based on Multiple Regression Analysis (MRA), we established the relationships between scenario variables, obtaining a variance of 60%–90% between the dependent and independent variables. The selected combination of supervised NLP techniques leverages an accuracy of 88.5%. We found that all regression models had at least one variable below the 0.05 threshold of the f − t e s t , therefore at least one statistically significant independent variable. These findings ultimately illustrate how citizens, taking the role of active social sensors, can yield vital data that authorities can use to make educated decisions and sustainably construct smarter cities. • We use a bespoke taxonomy of risks to detect events on social media in a smart city. • As active social sensors, citizens provide valuable insight for smart city management. • We determine general patterns and trends in citizen sentiment in a smart city context. • Our Machine Learning techniques show an accuracy of 88.5% for risk event detection. • We validate relationships between user satisfaction and events by Regression Analysis.

Data Analysis of Social Media’s Impact on COVID19 Pandemic Users’ Mental Health

Social media has a significant impact on people's daily lives and spread widely. Unrestrained usage of social media could have worsening consequences on mental health. The majority of COVID-19 users who were exposed to social media learned about numerous facts, which made their anxiety and depression-related mental health disorders worse. This study aims to determine how social media usage affects users' mental health during the COVID19 pandemic. Through surveys and expert interviews, this study collects both quantitative and qualitative data. Total number of respondent involved was 106 with average age group of 18-41 year old. Using reliability testing (Cronbach alpha test) and inferential statistic (Pearson Correlation and Chi Square), results show that during the COVID-19 pandemic, there is a significant link between social media use and mental health. Anxiety and depression brought on by social media are common among young adults, predominantly female, between the ages of 18 and 24, than in men. Additionally, correlation plot analysis with variety of queries reveal the mental health issues and activities on social media.

Spatial-Temporal Pattern Evolution of Public Sentiment Responses to the COVID-19 Pandemic in Small Cities of China: A Case Study Based on Social Media Data Analysis.

The impact of the COVID-19 pandemic on public mental health has become increasingly prominent. Therefore, it is of great value to study the spatial-temporal characteristics of public sentiment responses to COVID-19 exposure to improve urban anti-pandemic decision-making and public health resilience. However, the majority of recent studies have focused on the macro scale or large cities, and there is a relative lack of adequate research on the small-city scale in China. To address this lack of research, we conducted a case study of Shaoxing city, proposed a spatial-based pandemic-cognition-sentiment (PCS) conceptual model, and collected microblog check-in data and information on the spatial-temporal trajectory of cases before and after a wave of the COVID-19 pandemic. The natural language algorithm of dictionary-based sentiment analysis (DSA) was used to calculate public sentiment strength. Additionally, local Moran’s I, kernel-density analysis, Getis-Ord Gi* and standard deviation ellipse methods were applied to analyze the nonlinear evolution and clustering characteristics of public sentiment spatial-temporal patterns at the small-city scale concerning the pandemic. The results reveal that (1) the characteristics of pandemic spread show contagion diffusion at the micro level and hierarchical diffusion at the macro level, (2) the pandemic has a depressive effect on public sentiment in the center of the outbreak, and (3) the pandemic has a nonlinear gradient negative impact on mood in the surrounding areas. These findings could help propose targeted pandemic prevention policies applying spatial intervention to improve residents’ mental health resilience in response to future pandemics.

Analysing community reaction to refugees through text analysis of social media data

ABSTRACT Understanding the social integration of refugees requires scholars and community leaders to understand the complex and varied political reaction of citizens to the prospect and reality of refugees entering their local communities. In this study, we apply the Structural Topic Model (STM) to characterise citizen-level discourse in comments posted in response to refugee-related news articles on Facebook in Lancaster, Pennsylvania, and Roanoke, Virginia, two cities with similar demographics and conservative partisanship, but sharply contrasting refugee-related policies and experiences. We find that, overall, commenters framed their arguments with an identity-based frame more often than economics, morality, security or legality frames, but that these tended to be blended in ways that obscure the basis in identity. We also find that comments within the discourse of the more refugee-experienced Lancaster community were more likely to involve substantive arguments than in Roanoke, more likely to use economics frames, less likely to use identity frames, less likely to involve incivility and less likely to feature a salient misinformation-influenced theme (refugees vs. homeless veterans). This suggests that host community discourse grows more substantive and positive as a function of hospitable refugee policies and refugee hosting experience, and we discuss how this research might be expanded beyond this pair of cases to evaluate this broader implication.

Evolving norms: social media data analysis on parks and greenspaces perception changes before and after the COVID 19 pandemic using a machine learning approach

This study provides a novel approach to understand human perception changes in their experiences of and interactions with public greenspaces during the early months of COVID-19. Using social media data and machine learning techniques, the study delivers new understandings of how people began to feel differently about their experiences compared to pre-COVID times. The study illuminates a renewed appreciation of nature as well as an emerging but prominent pattern of emotional and spiritual experiences expressed through a social media platform. Given that most park and recreational studies have almost exclusively examined whether park use increased or decreased during the pandemic, this research provides meaningful implications beyond the simple extensional visit pattern and lends weight to the growing evidences on changing perceptions over and the positive psychological impacts of nature. The study highlights the preeminent roles parks and greenspaces play during the pandemic and guides a new direction in future park development to support more natural elements and nature-oriented experiences from which emotional and spiritual well-being outcomes can be drawn.

Circular business strategy challenges and opportunities for Industry 4.0: A social media data‐driven analysis

AbstractThis study contributes to state‐of‐the‐art circular business strategies (CBSs) for Industry 4.0 (I4.0) by designating the critical indicators since the advent of I4.0 and demonstrating the business challenges and opportunities for the global development of circularity‐based operations. Social media big data‐driven analysis is used to compare geographical regions, and suggestions are made to promote CBSs among regions. CBSs for I4.0 are receiving increasing attention, but a holistic viewpoint and an understanding of the indicators influencing their effective and successful implementation in each region are still lacking. To fill this gap, this study aims to determine the definitive CBS indicators for I4.0 and their opportunities and challenges across regions. As an enormous amount of multisource information is obtained from capitalizing on social media data analytics, firms can adopt social media data‐driven analysis, including content analysis, normal distribution tests, and the entropy weight method, to detect opportunities and challenges connected with the transformation to a CBS for I4.0. There are 28 validated indicators from the original set of relevant hashtags. The results show that the top indicators with a high level of difference among world regions selected for further discussion include waste management, vanguard leadership, deep learning, strategy execution, and collaboration. The findings of this study are a reference for decision‐makers to determine a suitable direction for CBS implementation, thus gaining sustainable competitive advantages.

Transformer-based deep learning models for the sentiment analysis of social media data

Sentiment analysis (SA) is a widely used contextual mining technique for extracting useful and subjective information from text-based data. It applies on Natural Language Processing (NLP), text analysis, biometrics, and computational linguistics to identify, analyse, and extract responses, states, or emotions from the data. The features analysis technique plays a significant role in the development and improvement of a SA model. Recently, GloVe and Word2vec embedding models have been widely used for feature extractions. However, they overlook sentimental and contextual information of the text and need a large corpus of text data for training and generating exact vectors. These techniques generate vectors for just those words that are included in their vocabulary and ignore Out of Vocabulary Words (OOV), which can lead to information loss. Another challenge for the classification of sentiments is that of the lack of readily available annotated data. Sometimes, there is a contradiction between the review and their label that may cause misclassification. The aim of this paper is to propose a generalized SA model that can handle noisy data, OOV words, sentimental and contextual loss of reviews data. In this research, an effective Bi-directional Encoder Representation from Transformers (BERT) based Convolution Bi-directional Recurrent Neural Network (CBRNN) model is proposed with for exploring the syntactic and semantic information along with the sentimental and contextual analysis of the data. Initially, the zero-shot classification is used for labelling the reviews by calculating their polarity scores. After that, a pre-trained BERT model is employed for obtaining sentence-level semantics and contextual features from that data and generate embeddings. The obtained contextual embedded vectors were then passed to the neural network, comprised of dilated convolution and Bi-LSTM. The proposed model uses dilated convolution instead of classical convolution to extract local and global contextual semantic features from the embedded data. Bi-directional Long Short-Term Memory (Bi-LSTM) is used for the entire sequencing of the sentences. The CBRNN model is evaluated across four diverse domain text datasets based on accuracy, precision, recall, f1-score and AUC values. Thus, CBRNN can be efficiently used for performing SA tasks on social media reviews, without any information loss.

Enhancing COVID-19 Epidemic Forecasting Accuracy by Combining Real-time and Historical Data From Multiple Internet-Based Sources: Analysis of Social Media Data, Online News Articles, and Search Queries.

BackgroundThe SARS-COV-2 virus and its variants pose extraordinary challenges for public health worldwide. Timely and accurate forecasting of the COVID-19 epidemic is key to sustaining interventions and policies and efficient resource allocation. Internet-based data sources have shown great potential to supplement traditional infectious disease surveillance, and the combination of different Internet-based data sources has shown greater power to enhance epidemic forecasting accuracy than using a single Internet-based data source. However, existing methods incorporating multiple Internet-based data sources only used real-time data from these sources as exogenous inputs but did not take all the historical data into account. Moreover, the predictive power of different Internet-based data sources in providing early warning for COVID-19 outbreaks has not been fully explored.ObjectiveThe main aim of our study is to explore whether combining real-time and historical data from multiple Internet-based sources could improve the COVID-19 forecasting accuracy over the existing baseline models. A secondary aim is to explore the COVID-19 forecasting timeliness based on different Internet-based data sources.MethodsWe first used core terms and symptom-related keyword-based methods to extract COVID-19–related Internet-based data from December 21, 2019, to February 29, 2020. The Internet-based data we explored included 90,493,912 online news articles, 37,401,900 microblogs, and all the Baidu search query data during that period. We then proposed an autoregressive model with exogenous inputs, incorporating real-time and historical data from multiple Internet-based sources. Our proposed model was compared with baseline models, and all the models were tested during the first wave of COVID-19 epidemics in Hubei province and the rest of mainland China separately. We also used lagged Pearson correlations for COVID-19 forecasting timeliness analysis.ResultsOur proposed model achieved the highest accuracy in all 5 accuracy measures, compared with all the baseline models of both Hubei province and the rest of mainland China. In mainland China, except for Hubei, the COVID-19 epidemic forecasting accuracy differences between our proposed model (model i) and all the other baseline models were statistically significant (model 1, t198=–8.722, P<.001; model 2, t198=–5.000, P<.001, model 3, t198=–1.882, P=.06; model 4, t198=–4.644, P<.001; model 5, t198=–4.488, P<.001). In Hubei province, our proposed model's forecasting accuracy improved significantly compared with the baseline model using historical new confirmed COVID-19 case counts only (model 1, t198=–1.732, P=.09). Our results also showed that Internet-based sources could provide a 2- to 6-day earlier warning for COVID-19 outbreaks.ConclusionsOur approach incorporating real-time and historical data from multiple Internet-based sources could improve forecasting accuracy for epidemics of COVID-19 and its variants, which may help improve public health agencies' interventions and resource allocation in mitigating and controlling new waves of COVID-19 or other relevant epidemics.