Analysis Of Social Media Data Research Articles

A machine learning approach for detecting customs fraud through unstructured data analysis in social media

Goods and services are sold through social media by individuals not authorized as legitimate dealers, resulting in lost taxes and customs duties to governments. This study proposes a model called SHIELD for detecting these violations through unstructured data in social media. The process involves collecting 2,373,570 records of commercial goods from social media platforms such as Twitter and Facebook in three phases. In Phase 1, keywords for labeling are collected for text classification. Three categories of results are defined: Red Line for smuggled goods, unpaid duty, prohibited goods, and restricted goods; Green Line for non-commercial goods; and Inspect for goods that cannot be identified from the text and require further investigation. Phase 2 and Phase 3 use keywords to detect smugglers from unstructured social media data for labeling grouped by three algorithms of Logistic Regression (LR), Gated Recurrent Unit (GRU), and Long Short-Term Memory (LSTM), employed to classify imported illegal products. The results of all tests show that the LSTM technique had the best accuracy of 99.44% and the best average F1 score of 90.55%. Using algorithms and techniques such as LR, GRU, and LSTM demonstrates the potential of machine learning and natural language processing in detecting illegal activities and promoting economic security.

Disastrous Event and Sub-Event Detection From Microblog Posts Using Bi-Clustering Method

Social media has become a nondetachable part of our life, with the exponential growth of usage in the past decade. Social sites like Twitter, Facebook, Instagram, Flickr, Weibo, etc., with their millions of user base, apart from being a source of entertainment, has proven to be a very useful mean for public opinion generation, news propagation and information broadcasting by authorities. Social media data analysis has been a popular research area for the past few years. Detecting subevents from social media posts to identify an unusual event that requires special attention, especially in a disaster situation, is one of the key researches in this domain. In this article, we have proposed a novel biclustering-based subevent detection method from the Twitter dataset for retrospective analysis of disaster events. First, we have clustered the data matrix using spectral co-clustering. Then we identified subevents (words) and formulated a ranking framework to find the top-ranked subevents within the clusters. Finally, through statistical analysis, we have shown that the proposed framework works better than other existing subevent detection methods.

Прикладний програмний інтерфейс як метод моніторингу даних соціальних мереж для досліджень у поведінковій економіці

In today’s behavioral economics, research based on social media data is becoming increasingly popular. Interactive platforms such as TikTok, Facebook, Instagram, X (Twitter), LinkedIn, and others generate a wealth of data that can be used to analyze user behavior. Processing large amounts of information requires the development of effective tools for collection and analysis of such data. The application programming interface (API) is one of the key methods that provides real-time access to data. The application programming interface allows scientists to automate the process of collecting information, customize queries according to various criteria, and obtain structured data for further analysis. The article examines the use of application programming interfaces as methods for monitoring social network data for research in behavioral economics. The features of the use of social networks for scientific research are analyzed. A monitoring method has been developed for collecting and processing data using APIs to study the behavior of social media users. The parameters of data collection (keywords, hashtags, time intervals, etc.) are determined, scripts for processing and storing data are created, a mechanism for starting the data collection process and monitoring its effectiveness and stability are designed. The proposed monitoring algorithm allows you to collect a representative database, analyze the content and interactions of social media users. APIs provide the ability to obtain a large amount of up-to-date and relevant information about user behavior, preferences, social interactions, and reactions to economic events, which allows for effective real-time data collection and analysis and increases the accuracy of forecasts in behavioral economics. Prospects for further research include the analysis of social media data, which will identify the impact of social and information factors on investment decision-making in the context of the market of virtual assets.

Enhanced Local and Global Context Focus Mechanism Using BART Model for Aspect Based Sentiment Analysis of Social Media Data

Enhanced Local and Global Context Focus Mechanism Using BART Model for Aspect Based Sentiment Analysis of Social Media Data

Application and Analysis of Social Media Data in Economic Research

As Internet technology enters a new era, social media has become an essential platform for people to obtain information and express their views, which has profoundly impacted economic research. This paper focuses on the application of social media data in economic research. First, this paper analyzes the essential performance and characteristics of social media data, such as user engagement, the speed of information diffusion, and content diversity. Second, it explores the expression of emotions on social media, including immediate reactions to economic events, analysis of the acceptability of economic policies, and the social psychology of economic issues. In addition, we analyze the reasons why social media data affects the economy, including the recommendation mechanism of algorithms, changes in social phenomena due to economic globalization, economic inequality and differentiation of public opinion among social classes, and distortion of economic opinions caused by information bubbles. Finally, we propose strategies to prevent social media data bias, including data cleaning and analysis, multi-dimensional information integration strategies, laws, regulations and technical measures, and education guidance. This paper aims to provide a comprehensive framework for analyzing social media data for economic research so as to promote the high- quality development of economic research.

Social Media Analytics and Business Intelligence: Leveraging Management Information System for Competitive Advantage

In today's ever-evolving business environment, the confluence of social media, analytics, and business intelligence has emerged as a powerful and transformative factor, fundamentally altering the way organizations function and compete. This summary provides an overview of the crucial function that Management Information Systems (MIS) serve in harnessing the potential of social media analytics and business intelligence to attain a competitive edge. The widespread adoption of social media platforms has led to an unprecedented influx of data, offering businesses profound insights into consumer behavior, preferences, and market dynamics. Nonetheless, the sheer scale of this data can be daunting in its raw state. MIS assumes a pivotal role in the collection, processing, and conversion of this data into actionable insights. This research delves into how organizations can effectively employ social media analytics, in synergy with business intelligence tools, to elevate their decision-making processes. Through the analysis of social media data, companies can gain a more profound comprehension of their target audience, keep a close watch on their brand's reputation, and pinpoint emerging trends and market opportunities. Business intelligence tools enable the visualization and interpretation of this data, providing stakeholders with the necessary information to make data-driven decisions. The competitive advantage derived from this approach is multifaceted. It allows organizations to respond proactively to market changes, align their products and services with customer demands, and allocate resources effectively. Furthermore, it enables organizations to optimize marketing campaigns, measure their ROI, and create personalized customer experiences, thereby improving customer satisfaction and loyalty. This study also examines the difficulties and moral factors linked to employing social media analytics and business intelligence. Matters of privacy, data security, and the ethical handling of customer data are pivotal elements that organizations need to confront in order to uphold public confidence and adhere to legal requirements. To sum it up, the amalgamation of social media analytics and business intelligence within the framework of Management Information Systems provides a potent set of tools for organizations striving to gain a competitive edge in the digital era. Leveraging the insights gained from social media data can lead to improved decision-making, enhanced customer experiences, and a sustainable edge in an ever-evolving market. This abstract outline the foundation for a comprehensive study that explores the practical implementation and strategies for harnessing these technologies effectively.

Evaluating random sampling bias in sentiment analysis of social media data

In this age marked by a wealth of information, the relevance of social networks has increased in a manner that is analogous to an exponential growth curve. Notably, the content that is shared on these platforms has the potential to act as a reflection of the emotional states that people are now experiencing. The importance of emotions is brought to light in the research presented here, which makes use of a technique based on a review of the relevant literature to analyze the problem of random sample bias and the effects that it has on sentiment analysis. It is possible to draw the conclusion, on the basis of the findings of the research, that the problem of random sample propensity is not a sporadic or insignificant one. In addition, the findings of the study indicate the presence of multiple types of prejudice. Because of the potential repercussions that could result from doing a distorted sentiment analysis, it is really necessary to keep your method focused.

Perancangan Konten Sosial Media Wisata Agro Lembah Kecubung

This research aims to design an effective social media content strategy to promote Kecubung Valley Agro Tourism. Agro-tourism is increasingly popular as a recreational destination, and social media has become a crucial platform for introducing and promoting tourism destinations. The study involved an in-depth analysis of agrotourism characteristics and target audience preferences. Through a content design approach that focuses on visuals, narrative and interaction, this research seeks to create an interesting experience for social media users. The research methodology includes surveys, interviews and analysis of social media data related to similar destinations. The results are expected to provide insight into user preferences, social media content trends, and the best strategies to increase the visibility of Lembah Agro Tourism. This research contributes to a practical understanding of how to design social media content that can increase user attraction and interaction, thereby advancing the promotion and marketing of agro-tourism destinations.

Social Media as a Tool for Understanding the Role of Motor Differences in Neurodivergent Identity and Lived Experience.

Social media offers an exciting opportunity for the field of motor development and behavior research. With platforms such as Twitter offering access to historical data from users' public bios and posts, there is untapped potential to examine community perspectives on the role of motor differences in identity and lived experience. Analysis of online discourse offers advantages over traditional qualitative methods like structured interviews or focus groups, including a less-contrived setting, global geographic and cultural representation, and ease of sampling. The aim of this special section is to present a pipeline for harvesting and analysis of Twitter data related to users' identities and discourse characteristics, specifically situated in the context of motor development and behavior. This pipeline is demonstrated in two independent studies, one on autistic users and one on developmental coordination disorder (DCD)/dyspraxic users. These studies demonstrate the utility of Twitter data for research on neurodivergent and disabled people's perspectives on their motor differences, and whether they are expressed as part of their identity. Implications of results are discussed for each study, as well as in the larger context of future research using a variety of approaches to analysis of social media data, including those from predominantly image- and video-based platforms.

Utilizing Social Media Data for Estimating Transit Performance Metrics

In this paper, a methodology using text and sentiment analysis of social media data is proposed to determine performance metrics that customers frequently mentioned when riding the New York Transit system and how they compare to those reported by the transit agency. This method is carried out by analyzing Twitter data collected from users mentioning their transit experience and recognizing trends regarding sentiment. The results show that customers mostly express negative over positive experiences about their transit services in terms of perceptions and expectations. Transit users are more critical about wait times and delays and notice the positive factors during service, such as system aesthetics, train speed, station cleanliness, and safety (in that order). The data analyzed also provides insights on locations and transit lines where there are frequent occurrences of customer issues. The proposed methodology provides a relevant contribution from a practical viewpoint because performance metrics are important factors to consider by transit operators which are exploring new practices to retain and attract ridership, given that transit systems are generally considered among the sustainable modes of transportation.

Stock Market Prediction Using Deep Reinforcement Learning

Stock value prediction and trading, a captivating and complex research domain, continues to draw heightened attention. Ensuring profitable returns in stock market investments demands precise and timely decision-making. The evolution of technology has introduced advanced predictive algorithms, reshaping investment strategies. Essential to this transformation is the profound reliance on historical data analysis, driving the automation of decisions, particularly in individual stock contexts. Recent strides in deep reinforcement learning algorithms have emerged as a focal point for researchers, offering promising avenues in stock market predictions. In contrast to prevailing models rooted in artificial neural network (ANN) and long short-term memory (LSTM) algorithms, this study introduces a pioneering approach. By integrating ANN, LSTM, and natural language processing (NLP) techniques with the deep Q network (DQN), this research crafts a novel architecture tailored specifically for stock market prediction. At its core, this innovative framework harnesses the wealth of historical stock data, with a keen focus on gold stocks. Augmented by the insightful analysis of social media data, including platforms such as S&P, Yahoo, NASDAQ, and various gold market-related channels, this study gains depth and comprehensiveness. The predictive prowess of the developed model is exemplified in its ability to forecast the opening stock value for the subsequent day, a feat validated across exhaustive datasets. Through rigorous comparative analysis against benchmark algorithms, the research spotlights the unparalleled accuracy and efficacy of the proposed combined algorithmic architecture. This study not only presents a compelling demonstration of predictive analytics but also engages in critical analysis, illuminating the intricate dynamics of the stock market. Ultimately, this research contributes valuable insights and sets new horizons in the realm of stock market predictions.

A Critical Review of the Effectiveness of Machine Learning & Deep Learning Approaches in Forecasting Stock Market Trends

For a long time, traders as well as researchers have been actively interested in financial market forecasting. A financial market prediction has an interest for investors & researchers since long time. An existing method for predicting stock prices often rely on technical and fundamental analysis, which have limitations in handling the complexity and volume of financial data. To handle complex data sets for forecasting the value of stock using new emerging learning techniques. The most commonly used approach such as artificial neural networks (ANN), neural network techniques, SVM, decision trees algorithm, and random forests. These techniques can capture complex nonlinear relationships between variables and adjust to changing market conditions. The machine learning & deep learning analyze the trend for the future values of stock prediction and provides observation for making decision. The importance of feature selection and data preprocessing are used for improving the prediction accuracy of stock value. The genetic method feature selection concept can reduce the dimensionality of the data and remove irrelevant features, while data preprocessing techniques such as normalization and scaling can improve the stability and convergence of the algorithms. Sentiment analysis of social media data can capture market sentiment and investor behavior, while news articles can provide insights into company performance and industry trends. The accuracy of predictions tends to decrease as the prediction horizon increases and the prediction accuracy varies across different stock markets. A lot of significant work, the most recent stock market-related prediction system includes multiple limitations. Now the conclusion, the projection of stock markets is a hybrid approach & certain parameters for stock market forecasting should be as accurate as possible.

What are the general public’s needs, concerns and views about energy efficiency retrofitting of existing building stock? A sentiment analysis of social media data

Energy efficiency retrofitting of existing buildings (EEREB) is critical to combating climate change. While policy interventions that seek to encourage widespread EEREB adoption benefit from a deeper understanding of public retrofit decision behaviour, studies that comprehensively evaluate the general public’s sentiments towards EEREB are lacking, contributing to a lack of understanding of the public’s needs, concerns and views. This study aims to evaluate the general public’s needs, concerns and views about EEREB. A total of 3,306 data of the general public’s views were collected from the social media platform YouTube, pre-processed and analyzed using the Model-based clustering and a text mining technique. Results showed nine areas of public concerns: ventilation, energy efficiency, indoor environment quality, comfort and occupant behaviour, cost involved, community engagement, technology use, implementation knowledge, and social impact. The general intention to retrofit is mostly driven by personal choices, not regulations per se, although some climate-dependant factors strongly impact public sentiments. Moreover, the recognition of benefits and public support for EEREB was attached to the understanding of cost saving and payback period and finding reliable supply-side actors for retrofit works, respectively. Overall, the public reported positive sentiments (56%) toward EEREB. Novel insights into the general public’s needs, concerns and views have been uncovered in this study, which policymakers can utilize to better advocate for EEREB.

Social Media Data Analysis: A Focus on the Government–Citizen Dynamic

Social Media Data Analysis: A Focus on the Government–Citizen Dynamic

Public perspective on renewable and other energy resources: Evidence from social media big data and sentiment analysis

To address global climate change, many countries are reducing CO2 emissions and replacing conventional energy resources with alternative ones. When developing national energy plans, it is essential to investigate public perspectives on the relationship between renewable and other energy resources. This study employs sentiment and correlation analyses of social media data to assess public perspectives on the complex relationships between renewable and other energy resources. The results show that renewable energy and nuclear energy have a complementary relationship in terms of positive emotions, but a substitute relationship in terms of negative emotions. These findings can inform regional and national energy plans and policies for renewable and nuclear energy. Additionally, this study demonstrates that the proposed methodology can be used to assess public perspectives on various energy resources.

Exploring Political Mistrust in Pandemic Risk Communication: Mixed-Method Study Using Social Media Data Analysis.

This research extends prior studies by the Finnish Institute for Health and Welfare on pandemic-related risk perception, concentrating on the role of trust in health authorities and its impact on public health outcomes. The paper aims to investigate variations in trust levels over time and across social media platforms, as well as to further explore 12 subcategories of political mistrust. It seeks to understand the dynamics of political trust, including mistrust accumulation, fluctuations over time, and changes in topic relevance. Additionally, the study aims to compare qualitative research findings with those obtained through computational methods. Data were gathered from a large-scale data set consisting of 13,629 Twitter and Facebook posts from 2020 to 2023 related to COVID-19. For analysis, a fine-tuned FinBERT model with an 80% accuracy rate was used for predicting political mistrust. The BERTopic model was also used for superior topic modeling performance. Our preliminary analysis identifies 43 mistrust-related topics categorized into 9 major themes. The most salient topics include COVID-19 mortality, coping strategies, polymerase chain reaction testing, and vaccine efficacy. Discourse related to mistrust in authority is associated with perceptions of disease severity, willingness to adopt health measures, and information-seeking behavior. Our findings highlight that the distinct user engagement mechanisms and platform features of Facebook and Twitter contributed to varying patterns of mistrust and susceptibility to misinformation during the pandemic. The study highlights the effectiveness of computational methods like natural language processing in managing large-scale engagement and misinformation. It underscores the critical role of trust in health authorities for effective risk communication and public compliance. The findings also emphasize the necessity for transparent communication from authorities, concluding that a holistic approach to public health communication is integral for managing health crises effectively.

Social media analytics for business-to-business marketing

Organizations continue to incorporate social media analytics into their routines to improve their performance outcomes. However, industrial marketing literature is lagging in that it has only explored the utility of social media as a promotional tool rather than as an analytical tool. Accordingly, this study is one of the first scholarly attempts to extend the concept of social media analytics into the industrial markets and advance a theoretical (vs. technical) perspective on social media analytics. Using the organizational learning lens, we define social media analytics as activities related to the acquisition and analysis of social media data and the dissemination, retention, and utilization of the findings. This theoretical study (i) guides future academic research by providing relevant questions and (ii) offers managerial implications of social media analytics, specifically, in the B2B sales.

Body Shame Twitter Movement Text Mining and Data Analysis by Applying MDCOR

As one of the most heated-discussed movements on Twitter these years, tweets in #bodyshame has been complicated and debatable. To explore the alternation of attitudes toward #bodyshame and how it has changed in the past decade, the study analyzes the data collected from three-time frames: 2012, 2017, and 2022, respectively. The increasing popularity and awareness of #bodyshame of the public are shown in this work. By applying MDCOR, an open-ended responses classification framework to data analysis, this paper was able to conclude the gradually alternating trend of Twitter users' attitudes toward body shame. Social media data analysis helps the understanding of the general development trend of #bodyshame movement, which ultimately provides a comprehensive overview of peoples acceptance and opinions towards various body types. This studys data-oriented research on attitudes towards body shame is profound in meaning.

Identifying factors associated with heightened anxiety during breast cancer diagnosis through the analysis of social media data on Reddit: a mixed methods study (Preprint)

Identifying factors associated with heightened anxiety during breast cancer diagnosis through the analysis of social media data on Reddit: a mixed methods study (Preprint)

Evolution of online public opinions on major accidents: Implications for post-accident response based on social media network

Based on the incredible breadth and speed of information distribution within social media network, and continuous development of natural language processing techniques for sentiment and topic analyses, it is potential to access online public opinions on major accidents. An integrated framework including four-stage evolution model, lexicon-based sentiment analysis, and latent Dirichlet allocation (LDA)-based topic extraction was developed based upon social media data analysis. The explosion of Xiangshui eco-chemical industrial zone (EXEIZ) was taken as a case for investigating the evolution of online public opinions. The large accident domain (LAD)-based sentiment dictionary was established for the lexicon-based approach for sentiment determination. Effective methods (i.e., combination of short microblogs and comments into a new text, and the hybrid approach integrating perplexity with principal component analysis) were deployed for overcoming two typical shortcomings (i.e., inappropriate for short text dataset, and sensitive to the number of topics within a corpus) of the LDA-based approach for topic analysis and extraction. According to the four-stage evolution model, customized strategies and suggestions were provided for guiding and controlling online public opinions on major accidents, in order to decrease their negative impacts on the society. This enabled conducting targeted communication efforts among different stakeholders for the reduction of negative sentiments such as indignation, sadness, and threat, and the avoidance of social media crises. Although a major accident poses tangible threats to the public, it may improve their awareness for preventing from these threats. In case of appropriate measures in time, the focus tends to steer toward effective and prosocial solutions. It is helpful for sustaining and re-establishing the image of authorities, enterprises or individuals that are closely associated with the major accident.