From Engineers' Tweets: Text Mining Social Media for Perspectives on Engineering Communication

In the internet age, social media and mobile devices are the most important tools of communication and marketing in the exhibition and event industry. Only a limited research has explored exhibition visitors’ engagement and preference perception through social networking media. Our study explores relationship between Facebook fan pages and visitor engagements of the exhibitions. The study found that the number of visitors, Facebook fans like counts, comment counts, and emotional factors (sentiment polarity) have a significant positive correlation. On the other hand, numbers of visitor rapidly grow every year, but comment sentiment polarity of a positive emotion drops every year. This reduction is the warning sign that the exhibition management and marketing strategy need to be improved in order to continue to have a positive visitor engagement emotion. Our linear Discriminate Analysis (LDA) result found that visitors were highly concerned with gamification activities for an incentive prize both to create involvement and to enhance exhibition experience. Our finding also shows the relevance of both FB like counts and comments generated by social media in relation to the visitor engagement performance. This pioneer study utilized both text mining and sentimental analysis into the exhibition study, especially the area of Asian research.

PurposeOnline customer reviews could shed light into their experience, opinions, feelings, and concerns. To gain valuable knowledge about customers, it becomes increasingly important for businesses to collect, monitor, analyze, summarize, and visualize online customer reviews posted on social media platforms such as online forums. However, analyzing social media data is challenging due to the vast increase of social media data. The purpose of this paper is to present an approach of using natural language preprocessing, text mining and sentiment analysis techniques to analyze online customer reviews related to various hotels through a case study.Design/methodology/approachThis paper presents a tested approach of using natural language preprocessing, text mining, and sentiment analysis techniques to analyze online textual content. The value of the proposed approach was demonstrated through a case study using online hotel reviews.FindingsThe study found that the overall review star rating correlates pretty well with the sentiment scores for both the title and the full content of the online customer review. The case study also revealed that both extremely satisfied and extremely dissatisfied hotel customers share a common interest in the five categories: food, location, rooms, service, and staff.Originality/valueThis study analyzed the online reviews from English-speaking hotel customers in China to understand their preferred hotel attributes, main concerns or demands. This study also provides a feasible approach and a case study as an example to help enterprises more effectively apply social media analytics in practice.

The growing incidents of counterfeiting and associated economic and health consequences necessitate the development of active surveillance systems capable of producing timely and reliable information for all stake holders in the anti-counterfeiting fight. User generated content from social media platforms can provide early clues about product allergies, adverse events and product counterfeiting. This paper reports a work in progresswith contributions including: the development of a framework for gathering and analyzing the views and experiences of users of drug and cosmetic products using machine learning, text mining and sentiment analysis, the application of the proposed framework on Facebook comments and data from Twitter for brand analysis, and the description of how to develop a product safety lexicon and training data for modeling a machine learning classifier for drug and cosmetic product sentiment prediction. The initial brand and product comparison results signify the usefulness of text mining and sentiment analysis on social media data while the use of machine learning classifier for predicting the sentiment orientation provides a useful tool for users, product manufacturers, regulatory and enforcement agencies to monitor brand or product sentiment trends in order to act in the event of sudden or significant rise in negative sentiment.

Social media has become a major aspect of online activity, and thus an essential part of cybercrime and cyber terrorism-related operations. As LEA’s (law enforcement agencies) focus upon cybercrime and cyber terrorism threats increase, so does the requirement to consider the potential application of social media as a vital aspect of any cyber defense strategy. This chapter considers social media's role in society and the characteristics that influence the way people use it. A number of case study scenarios are presented to demonstrate the potential application of social media in combatting a variety of criminal threats including human trafficking, hostage situations, and other organized crime. Each scenario considers the potential impact of a number of example technologies such as text mining, NLP (natural language processing) and sentiment analysis supported by an understanding of the relevance of people's usage characteristics and behaviors within social media.

People use social media as free channels to share their sentiments and experiences during all stages of consuming a product or service. Likewise, corporations depend on social media as feedback sources that influence the positioning of their products/services in the market. This paper aims to recognise the frequent product flaws and warranty issues through social network mining. We have performed ontology-based methods, text mining, and sentiment analysis using deep learning methods on social media data to investigate product failures, symptoms, and the correlation between warranty programs and customer behaviour. Correspondingly, a multi-sources mining approach has been incorporated into social media mining to cover all the occasions. Furthermore, we promoted a decision support system to learn practically through customer feedback. Finally, to validate the accuracy and reliability of the results, we used the claimed data of the laptop industry to compare our derivatives and machine learning validation metrics to ensure accuracy.

Social media is a remarkable outcome of Web 2.0 technology, which is very popular among the Internet users. The general public is using social media as a communication media to fulfil their information requirements on various occasions such as disaster communication, health communication, marketing products and services and political campaigns. However, the openness of social media provides a great platform for misinformation sharing which is a very common problem in social media communication. Hence, the main objectives of this study are to analyse the publication year and total citation count of publications on misinformation on social media and to identify the main disciplines of misinformation studies on social media using the text mining technique. The primary data of this study were extracted from the Web of Science database using the keywords; social media, misinformation, disinformation and fake news on 16th April 2020. The WoS provided 62 search results and all 62 articles were considered in this study. The title of the article, journal, published year, total citation, abstract, author keywords, keywords plus, Web of Science categories, and research areas were extracted from WoS database. The text mining was done manually. According to the results, scholarly publications on misinformation on social media were first published in the year 2012. Scholarly publications were categorized into 10 main categories; Information, Media, Medical information, Social Science, Communication, Health information, Computer science, Other Sciences, Engineering and Management and Finance. The medical information subject area is covering vast varieties of research areas than the other main subject areas.

Gigantic amount of content is generated every second on the web. Blog posts, tweets, videos, uploaded images maintain the rich and increasing content of the social media. Great amount of information is obtained from social media through text mining techniques which are being improved and developed continuously. Due to the increasing variety of social media, data is flooding into every business and techniques of gathering information from unstructured data becomes a challenge. Processing and analyzing of large collections of social media text is difficult by using traditional single computer methods. Fortunately, with each passing day, the development and improvements in Big Data infrastructure are promising and the analysis capabilities are also increasing. This infrastructure supports language independent researches on text mining. There can be diversified searches with language-specific definitions and assumptions. Since many of the studies on social media text mining analysis focus on English language, this paper would help enhance text mining efficiently in Turkish language which is a word order free agglutinative language. The linguistic aspects of Turkish language make text mining complicated. Suffixes can change the word meanings easily. Word stemming becomes elaborated due to indigenous letters. The intent of this article is to demonstrate the issues, methods and applications of qualitative approaches that enable text mining in Turkish language considering its compelling features with covering preprocessing, feature selection and topic clustering on text mining considering Turkish linguistic features.

Should Doctors Be More Careful with Social Media?

IntroductionSince 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.MethodsThe 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.ResultsAdolescents 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.ConclusionDuring 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.

Behavioral economics and behavioral finance believe that public mood is correlated with economic indicators and financial decisions are significantly driven by emotions. A growing body of research has examined the correlation between stock market and social media public mood state. However most research is conducted on English social media websites, the number of research on how public mood states in Chinese social media websites affect the stock market in China is limited. This paper first summarizes the previous research on text mining and social media sentiment analysis. After that, we investigate whether measurements of collective public mood states derived from Weibo which is a social media website similar as Twitter but most posts are written in Chinese are correlated to the stock market price in China. We use a novel Chinese mood extracting method using two NLP (Natural Language Processing) tools: Jieba and Chinese Emotion Words Ontology to analyze the text content of daily Weibo posts. A Granger Causality analysis is then used to investigate the hypothesis that the extracted public mood or emotion states are predictive of the stock price movement in China. Our experimental results indicate that some public mood dimensions such as “Happiness” and “Disgust” are highly correlated with the change of stock price and we can use them to forecast the price movement.

Gigantic growth of text mining is becoming a potential source of crowd wisdom extraction and analysis especially in terms of text pre-processing and analysis. The ubiquitous growth in the text mining field is unprecedented where social media mining is playing a significant role. Therefore, it’s a significant task of current research purview which not only provides an appropriate platform for the contributors but also for active information seekers. Text mining has gained the widespread attention of internet users which is almost on an explosion level. Accessibility of data on such a behemoth scale mandates regular and critical analysis of this information for various perspectives’ plausibility. Rich patterns of online social text can be exploited to extract relevant information effectively. As text mining plays a significant and crucial role in discovery of these insights, therefore its challenges and contribution to social media analysis must be explored extensively. In this chapter, we provide a brief summary of the modern state of text mining in pre-processing and machine learning mechanism for COVID-19. Emphasis is placed on the resources and learning mechanism available to researchers and practitioners, as well as the major text mining tasks of interest to the community. Thus, the main aim of this chapter is to expound and intellectualize the domains of social media which are accessible on an extraordinary range in the field of text mining for COVID-19.

The large-scale introduction of computers into professional and everyday human activities, the formation of a global mass communication system based on the Internet and the formation of a single network space formed the basis of a modern social communication system. But at the same time, despite the long period of their development, there was no unambiguous concept denoting the phenomenon of Internet-based media, which includes means of communication, as well as the social-network interaction environment formed by them. The authors analyze four concepts used to display the media phenomenon: (1) information and communication environment, (2) social network space, (3) media space and (4) public communication space. Analysis shows that each of these concepts focuses on a specific aspect of the communications of modern society. The information and communication environment is a semi-structured set of computers and information and telecommunication technologies that allow organizing actor interactions to coordinate their activities in the storage, transmission, processing of information. A social network space is rhizome from social media, which allows users to organize online communication and produce user-generated content. Media space is a complex configuration of social media related to the production and consumption of media, the unity of which, within the framework of the information picture of the world, holds a certain social space together. The public communications space is a set of social media aimed at mainstreaming socially significant problems and discussing in the social-network space.

Enhancing financial performance with social media: An impression management perspective

As more Americans use Facebook as one of their primary sources of news consumption, television broadcasters have increasingly added this social media platform in their distribution strategies for program optimization and audience engagement. Within a local market, media outlets not only need to monitor and analyze their own social media content and online audience behavior but also their competitors to increase competitive advantage. This study proposes a social media competitive analysis framework with three aspects: social media utilization, social media content strategy, and social media news engagement. By applying the analytics framework, this study examines the dynamic Facebook competition among five local television stations within a designated market area (DMA) in the U.S. The results show two divergent social media deployment patterns among these five local television broadcasters. Theoretical and practical implications are discussed to help media organizations develop their own social media strategies and gain competitive advantages over their opponents.

In this paper, secondary crisis communication have been analyzed with respect to the effects of traditional media vs new media tools during the times of crises. The results confirms the strong relationship between the variables of SMCC model- Social Mediated Crisis Communication model. As the model highlighted the importance of word of mouth during crisis time, the results testing the cognitive dimension of acquiring news and updates indicated that the major source of information about the true crisis that happened before conducting the research was “friends and family” and social media –via what’s app and BBM. When testing the affective dimension, comparing traditional media to social media in terms of trustworthiness, the traditional media won over social media. Word of mouth also plays an important role in evaluating news as trustworthy. The direct and indirect channels of communication that transfer information about crisis appeared in testing the behavioral component of the information processing, where information is processed either directly by the receiver through social media or traditional media, or indirectly via inactive users who receive information reported by followers via twitter or Facebook or what’s app. The above mentioned results point to the need to extend crisis communication theories and shed more light on the mediated role of social networking sites in crisis communication as a shift from one to many communication mode to many to many communication mode. There are many intervening variables in the process of new media crisis communication that need to be examined and experimented such as interpretations use experiences (Schultz et al.,2011) in addition to users’ evaluation of medium credibility and the overall communication environment. Integration of social media into crisis and risk communication is crucial for crisis management, but it needs to be well planned. Using social media has the advantage of gaining fast information that would reduce rumors, but it has to be well planned to gain trust and credibility Having a comparison of a non-crisis information seeking situation builds the credibility of the results in times information seeking during crisis situations. The findings of this study have practical implications when designing communication strategies for government and organizations during emerging crisis situations. Using social media would result in a more positive evaluation of the situation, and achieving higher levels of awareness when information dissemination is needed. Communication of consistent messages via social networking sites in line with traditional media forms could assist in limiting rumors and achieve news credibility.