Social Data Mining Research Articles

How do consumers evaluate sponsorship in the aftermath of sport celebrity scandals? Uncovering evidence on social media data mining

PurposeThe purpose of this study is to investigate the effects of sport scandals on consumer perceptions of the associated sponsors and sport and to provide a typology of sport celebrity scandals to guide management response tactics.Design/methodology/approachThe study conducted four focus groups that were followed by social media data mining. A total of 8,289 consumer comments were collected from 147 websites, and a total of 224 comments were analyzed in terms of themes and frequency.FindingsThe research found the impact of sport scandals on consumer perceptions of sponsorship evaluations depended on whether the scandal was gender related scandal, recreational drug use, gender violence, unplanned and planned on-field scandals. Gender violence and planned on-field scandals can have an overwhelmingly negative impact on sponsorship evaluations, while unplanned on-field scandals may result in positive effects. Consumer empathy may influence the impact of recreational drug use, and the gender of the sport celebrity can influence the impact of unplanned on-field scandals.Research limitations/implicationsThis study contributes to sponsorship theory by indicating the type of scandal affects consumer perceptions of associated sponsors and sport.Practical implicationsThe findings may guide management to develop response tactics to sport scandals. The response tactics may be based on consumer perceptions of the impact of the scandal on the associated sponsors and sport. Sponsor and sport management response tactics may be perceived as a differentiation of the sponsor and sport brands. It may be necessary that sponsorship agreements included pre-determined response tactics that contribute to value formation in the local community.Originality/valueThis study contributes to sponsorship theory by indicating the type of scandal affects consumer perceptions of sponsorship evaluations. Two additional factors may impact these influences: consumer empathy and the gender of the sport celebrity.

Learning Agents in Robot Navigation: Trends and Next Challenges

Multiagent reinforcement learning performs well in multiple situations such as social simulation and data mining. It particularly stands out in robot control. In this approach, artificial agents behave in a system and learn their policies for their own satisfaction and that of others. Robots encode policies to simulate the performance. Therefore, learning should maintain and improve system performance. Previous studies have attempted various approaches to outperform control robots. This paper provides an overview of multiagent reinforcement learning work, primarily on navigation. Specifically, we discuss current achievements and limitations, followed by future challenges.

Social big data mining for the sustainable mobility and transport transition: findings from a large-scale cross-platform analysis

The paper reports findings from a study that examining how cross-platform social media analysis can help to map the digital discourse on sustainable mobility and sustainable transport, and enhance the understanding of sociotechnical low-carbon transport transitions. Using the hashtag search queries #sustainabletransport and #sustainablemobility, 33,121 Tweets (2013–2021) and 8,089 Instagram images including captions (2017/2018–2021) were scraped using the Python modules Twint and Instaloader. Quantitative text and sentiment analyses were applied to the Tweets and image captions. Additionally, an automated machine learning-based image analysis of the Instagram images was conducted using object detection via OpenCV. Synthesized results formed the base for a cross-platform analysis inspired by Rogers’ method comprising hot topics/key themes, user mentions, sentiment polarity, and co-hashtags. Notably, electromobility emerged as a prominent theme, particularly on Instagram, while #sustainabletransport was closely associated with active travel, notably bicycling, and #sustainablemobility showcased a dominance of electromobility discourse. The study demonstrates the investigative potentials of cross-platform social media analysis studies to enhance the understanding of sociotechnical low-carbon transport transitions. Drawing on key results, the paper suggests an adapted version of the Geelsean Multi-Level Perspective on Sociotechnical Transitions.

A Survey of Cutting-edge Multimodal Sentiment Analysis

The rapid growth of the internet has reached the fourth generation, i.e., web 4.0, which supports Sentiment Analysis (SA) in many applications such as social media, marketing, risk management, healthcare, businesses, websites, data mining, e-learning, psychology, and many more. Sentiment analysis is a powerful tool for governments, businesses, and researchers to analyse users’ emotions and mental states in order to generate opinions and reviews about products, services, and daily activities. In the past years, several SA techniques based on Machine Learning (ML), Deep Learning (DL), and other soft computing approaches were proposed. However, growing data size, subjectivity, and diversity pose a significant challenge to enhancing the efficiency of existing techniques and incorporating current development trends, such as Multimodal Sentiment Analysis (MSA) and fusion techniques. With the aim of assisting the enthusiastic researcher to navigating the current trend, this article presents a comprehensive study of various literature to handle different aspects of SA, including current trends and techniques across multiple domains. In order to clarify the future prospects of MSA, this article also highlights open issues and research directions that lead to a number of unresolved challenges.

Public emotions and visual perception of the East Coast Park in Singapore: A deep learning method using social media data

Social media data mining has become a prevailing approach to understanding people’s emotional response to, and perception of, public spaces. Drawing on Google Maps Reviews’ text reviews and shared images, this study develops a deep learning framework that combines Transformer BERT and CNN-VGG models to explore public emotions and visual perception of the East Coast Park (ECP) in Singapore. The results show that (1) public emotions of the ECP are predominantly joy, with minor emotional fluctuations in 2020 due to the pandemic lockdown; (2) CNN-VGG image classification is an effective tool to capture people’s visual preferences of public spaces, (3) public emotions across 12 specific public spaces are predominantly joy and neutral, but the results of people’s visual preference exhibited considerable diversity, and (4) image data is mostly associated with joy or neutral emotions, generally indicating users’ satisfaction of ECP public space, and for this reason, there is a lack of sound base to identify environmental factors associated with people’s negative emotions. This study shows that the proposed deep learning framework is effective in using social media data to understand the public's emotional response to and visual preference of urban public spaces.

Luxury Brands and Social Media Drivers and Outcomes of Consumer Engagement on Instagram

This research aims to identify the factors that drive consumer engagement with luxury brands on Instagram and analyze the outcomes of consumer engagement. It will use an interpretive research philosophy, an inductive approach, a combination of secondary and primary data, a qualitative methodology, and semi-structured interviews and questionnaires to examine the drivers and outcomes of consumer engagement on Instagram with luxury brands. The results of the research suggest that luxury brands should invest in innovative and visually appealing content to appeal to their audience and create a strong brand identity. Additionally, personal characteristics such as age, gender, and income level also influence the relationship between engagement and loyalty. Future researchers should use social media analytics and data mining techniques to gain insights into customer behavior, preferences, and sentiments, and conduct longitudinal studies to examine the long-term effects of social media on relationship marketing.

Belief Mining in Persian Texts Based on Deep Learning and Users' Opinions (revised December 2022)

Belief mining and the study of public opinion provide valuable information. Analyzing the feelings and belief mining of social media data leads to understanding users' opinions and has wide applications in decision making and policymaking. This article applies a new method based on deep learning to solve the problems of belief mining for Persian comments on the Twitter. In this method, first, the data is preprocessed with a deep neural network and then classified into political, cultural, economic, and sports classes, and the sentimental polarity is obtained. SentiPers is applied on four different datasets from Persian Twitter, Digikala store, Google translator, and synonym for evaluation. Then the results are compared with other machine learning and deep learning methods such as neural network, support vector machine, DNN, CNN, and LSTM. Python software has been used to implement this method. The accuracy of the proposed word embedding method for LSTM, CNN, DNN on the SentiPres dataset is 0.931, 0.923, 0.916 respectively. For the TF-IDF method, it is 0.837, 0.863, 0.883 respectively. that the accuracy of LSTM-WSD, CNN-WSD model has increased by 8% and 6% compared to TF-IDF. The results show that the LSTM and Word embedding methods work best.

The influence of social media, big data, and data mining on the evolution of organizational behavior: Empirical study in Jordanian telecommunication sector

The aim of this study was to evaluate the impact of social media, big data, and data mining on the development of organizational behavior within the telecommunications industry in Jordan. The main objective of this study was to investigate the effects of technological components on the alteration of organizational behavior in the communications sector of Jordan. To accomplish this objective, a thorough empirical investigation was undertaken, encompassing the collecting of data from key stakeholders within the telecommunications sector in Jordan. A sample size of 412 participants, encompassing people from diverse roles within the communications sector, was chosen for the purpose of this study. The participants' replies and perspectives were gathered via the administration of surveys and conducting interviews, resulting in a comprehensive data set suitable for analysis. This study investigated the intricate relationship between the utilization of social media, the application of big data analytics, and the implementation of data mining techniques in influencing the dynamics of organizational behavior. The study's results underscored the substantial impact that social media platforms have on communication patterns and collaboration within telecommunication firms. Furthermore, the utilization of big data analysis has emerged as a significant catalyst for the enhancement of informed decision-making processes, exerting influence on diverse facets of organizational behavior, including strategic planning, employee engagement, and customer interactions. Data mining techniques have been identified as having a crucial function in extracting significant patterns and trends from extensive datasets, hence helping to the improvement of organizational learning and adaptation. The research findings indicated that the incorporation of social media, big data, and data mining technologies had a beneficial effect on the development of organizational behavior within the telecommunications industry in Jordan. The findings underscore the importance for enterprises to proactively utilize these technologies to cultivate a work environment that is characterized by increased agility, responsiveness, and collaboration. This study provides significant contributions to the subject of organizational behavior by examining the impact of social media, big data, and data mining within the specific context of the telecommunication sector in Jordan. The research sheds light on the transformative consequences of these technological advancements. The consequences of these findings have broad relevance for organizational leaders, politicians, and researchers, serving as a basis for further investigations in the dynamic realm of technology-driven organizational behavior.

Research on Weibo marketing advertising push method based on social network data mining

Research on Weibo marketing advertising push method based on social network data mining

Prediction method of e-commerce consumers' purchase behaviour based on social network data mining

Prediction method of e-commerce consumers' purchase behaviour based on social network data mining

Research on Weibo marketing advertising push method based on social network data mining

Research on Weibo marketing advertising push method based on social network data mining

Online learning effectiveness evaluation for college students based on social network data mining

Online learning effectiveness evaluation for college students based on social network data mining

CareerMiner: Automatic extraction of professional network from large Chinese resume data

The professional contacts of a person, including their past colleagues and supervisors, can play an important role in job recommendation and intelligent human resources management. However, collecting such information on a large scale can be challenging and costly. In this paper, we propose CareerMiner, a system to automatically construct a dynamic professional network to capture people’s colleague and supervisor relationships based on inter-related work experiences in a large collection of Chinese resumes. Specifically, we extract fine-grained work affiliation and job title information from resume text using a customized Name Entity Recognition model and introduce a trie-like data structure to efficiently index the hierarchical affinities of the implicit organizational structure in work experiences. Then, the topology of leaf nodes is used to identify professional relationships. Experimental results on real-world resume data are presented to demonstrate the effectiveness of the proposed system. Additionally, we develop an open-source demo system that includes the main features of the system. We also conduct a case study to illustrate how CareerMiner can be utilized for the social science research and data mining applications of professional networks.

Korean adolescents' coping strategies on self-harm, ADHD, insomnia during COVID-19: text mining of social media big data.

Since the Coronavirus disease 2019 (COVID-19), public safety measures, including social distancing and school closures, have been implemented, precipitating psychological difficulties and heightened online activities for adolescents. However, studies examining the impact of the pandemic on adolescent mental health and their coping strategies in Asian countries are limited. Further, most studies have used survey measures to capture mental health challenges so far. Accordingly, this study aimed to examine the psychological challenges South Korean adolescents experienced and their coping strategies during the pandemic using the Natural Language Processing (NLP) and Text mining (TM) technique on adolescents' social media texts/posts. The data were gathered from social media texts/posts such as online communities, Twitter, and personal blogs from January 1, 2019, to October 31, 2021. The 12,520,250 texts containing keywords related to adolescents' common psychological difficulties reported during the pandemic, including self-harm, Attention-Deficit/Hyperactivity Disorders (ADHD), and insomnia, were analyzed by TM, NLP using information extraction, co-occurrence and sentiment analysis. The monthly frequency of the keywords and their associated words was also analyzed to understand the time trend. Adolescents used the word "self-harm" in their social media texts more frequently during the second wave of COVID-19 (August to September 2020). "Friends" was the most associated word with "self-harm." While the frequency of texts with "Insomnia" stayed constant throughout the pandemic, the word "ADHD" was increasingly mentioned in social media. ADHD and insomnia were most frequently associated with ADHD medications and sleeping pills, respectively. Friends were generally associated with positive words, while parents were associated with negative words. During COVID-19, Korean adolescents often expressed their psychological challenges on social media platforms. However, their coping strategies seemed less efficient to help with their difficulties, warranting strategies to support them in the prolonged pandemic era. For example, Korean adolescents shared psychological challenges such as self-harm with friends rather than their parents. They considered using medicine (e.g., sleeping pills and ADHD medication) as coping strategies for sleep and attention problems.

Optimization Model Construction of Online Public Opinion Dissemination Based on Behavioral Data Mining

Abstract In this paper, we propose an optimization method based on behavioral data mining for the emergence of negative emotions in network public opinion in unexpected situations leading to problems that endanger social stability and use data mining to confirm the correspondence of the kNN algorithm. In the opinion propagation model, the radius of opinion radiation is assumed to use the network node density and distribution density as features of node exchange information. Then, the kNN algorithm is used to train the comment set for the analysis of user sentiment evolution of network opinion in the social network environment, and the web crawler technology is used to obtain the output interface data APIs of microblogs and WeChat, and the social media user comment data is used as the data for the empirical analysis of network opinion. snowNLP and kNN algorithms are used to analyze the sentiment score of network opinion and the sentiment score less than 0.5, i.e., the sentiment polarity. There were 15 days when the sentiment score tended to be negative and 65 days when the sentiment score was greater than 0.5, i.e., the sentiment polarity tended to be positive.

A MapReduce-based approach to social network big data mining

The rapid development of social networks has facilitated the convenience of users to receive information. As a network communication platform for people’s daily use, microblog has countless information data. In view of the low efficiency and poor clustering effect of K-means algorithm, a parallel K-means clustering algorithm based on MapReduce model is studied; In order to alleviate the difficulty in calculating the similarity of microblog topic text, the space vector model and semantic similarity are used to calculate the similarity between texts to improve the quality of microblog text classification. The data expansion rate of corresponding nodes under different data sets shows that the average expansion rate of the parallel K-means algorithm reaches 0.89, and the running rate is the highest. The results show that the parallel K-means algorithm has good clustering stability and the highest clustering quality, reaching 1.24; The clustering time of the algorithm is the shortest, the average clustering time is 1.27 minutes, and the clustering effect and efficiency of the algorithm are the best. In the quality analysis of Weibo topic recommendation, the accuracy of P-K-means recommendation is 95.64%, user satisfaction is 98.64%, and the recommendation effect is also the best. It shows that the research on the parallel K-means clustering algorithm based on MapReduce has the best performance in microblogging topic mining and recommendation, which can efficiently recommend topics of interest to users and enhance users’ microblogging experience.

Depression in South Korean Adolescents Captured by Text and Opinion Mining of Social Big Data.

Depression in adolescence is recognized as an important social and public health issue that interferes with continued physical growth and increases the likelihood of other mental disorders. The goal of this study was to examine online documents posted by South Korean adolescents for 3 years through the text and opinion mining of collectable documents in order to capture their depression. The sample for this study was online text-based individual documents that contained depression-related words among adolescents, and these were collected from 215 social media websites in South Korea from 1 January 2012 to 31 December 2014. A sentiment lexicon was developed for adolescent depressive symptoms, and such sentiments were analyzed through opinion mining. The depressive symptoms in the present study were classified into nine categories as suggested by the Diagnostic and Statistical Manual for Mental Disorders, 5th Edition (DSM-5). The association analysis and decision tree analysis of data mining were used to build an efficient prediction model of adolescent depression. Opinion mining indicated that 15.5% were emotionally stable, 58.6% moderately stressed, and 25.9% highly distressed. Data mining revealed that the presence of depressed mood most of the day or nearly every day had the greatest effect on adolescents' depression. Social big data analysis may serve as a viable option for developing a timely response system for emotionally susceptible adolescents. The present study represents one of the first attempts to investigate depression in South Korean adolescents using text and opinion mining from three years of online documents that originally amounted to approximately 3.1 billion documents.

Analysis of Social Network Data Mining for Security Intelligence Privacy Machine Learning

The Modern communication on the Internet platform is most responsive through social media. Social media has changed and is still reshaping how we share our thoughts and emotions in communication. It has introduced a constant real-time communication pattern that was before unheard of. Young and old, organizations, governmental agencies, professional associations, etc., all have social media accounts that they use exclusively for communication with other users. Social media also acts as a powerful network engine that connects users regardless of where they are in the world. The development of global communication will greatly benefit from the availability of this new communication platform in the future. Consequently, there is a pressing need to research usage trends. Therefore, it is vital to investigate social media platform usage trends in order to develop automated systems that intelligence services can use to help avert national security incidents. Through the use of social media data mining, this research study suggests an automated machine learning model that can improve speedy response to crises involving national and International security.

Applied Techniques for Twitter Data Retrieval in an Urban Area: Insight for Trip Production Modeling

This paper presents methods of retrieving Twitter data, both streaming and archive data, using Application Programming Interfaces. Twitter data are a kind of Location Based Social Network Data that, nowadays, is emerging in transportation demand modeling. Data regarding the locations of trip makers represent the most crucial step in the modeling. No research article has specifically addressed this topic with an up-to-date method; hence, this paper aims to refresh methods for retrieving Twitter data that can capture relevant data. The method is unique as the data are gathered for trip production modeling in zonal urban areas. Python script programs were built for both data retrieving methods. The programs were run for streaming data from May 2020 to April 2021 and archive data from 2018. The data were collected within Serang City, which is the nearest provincial city to Jakarta, the capital of Indonesia. In order to gather streaming data with no loss, the program has been run with referencing on sub-district office coordinate locations. Retrieving the intended data produces 1,090,623 documents, of which 54,103 are geotagged data from 2495 users. The study concluded that streaming data produce more geolocation data, while historical data capture more Twitter user data with relatively very little geotagged data and greater textual data than the period covered in this research. Thus, both techniques of retrieving Twitter data for urban personal trip modeling are necessary. Obtaining sufficient data collection using data streaming retrieval resulted in the most effective data preprocessing. This research contributes to Location Based Social Network data mining knowledge, both geolocation and text mining, and is useful for insight into developing trip production modeling in passenger transportation demand modeling using Machine Learning. This study also aims to provide useful methods for transportation system researchers and data scientists in utilizing Location Based Social Network data.

Анализ рынка продукции северного оленеводства России на основе анализа массивов больших данных

Reindeer products have become an integral element of the culture of indigenous peoples in the Arctic region, playing a significant role in human adaptation to extreme climatic conditions. This study investigated the online sales offerings of reindeer products to identify the Far North regions in Russia that most actively use information and communication technologies to promote their products in the domestic regional and interregional markets. It also aimed to analyze the range of traditional Arctic food products. The research methodology drew on the theoretical approaches of the spatial economics by A. G. Granberg and the free trade theory by P. R. Krugman. Methods for analyzing big data based on social media data mining were applied. The study utilized messages on the Internet related to the sale of reindeer products in Russia’s Far North regions in 2021–2022. The novelty of this study lies in the implementation of big data collection and analysis methods. It required adapting the PolyAnalyst platform to the needs of a specific research field and improving the analysis methodology. The analysis of online sales offerings revealed the most active Arctic regions: the Murmansk region, the Yamalo-Nenets Autonomous Okrug, the Komi Republic, and the Chukotka Autonomous Okrug. The growing interest in the Arctic has contributed to the formation of a wide range of ready-made dishes and venison processing methods, also becoming a catalyst for growth in Arctic food tourism. This creates important conditions for the economic growth of rural Arctic territories. Online platforms expand opportunities for Arctic ethnic entrepreneurship by overcoming the relative isolation of Arctic economic systems and providing a venue for selling products on the Internet. Consequently, future research prospects involve using big data analysis of the reindeer husbandry market to develop measures for managing the sales of reindeer products.