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Travelers' perceptions of hotels and their aspects have been the focus of much research and are often studied by analyzing consumers' online reviews. Yet, little attention has been given to the effect of the trip mode, i.e., whether the person travels alone or with others, on travelers' preferences as sentiment. Here, we study the influence of the trip mode and purpose using a mixed-methods approach. We conducted a user study to evaluate the perceptions of reviews across trip modes and found that star ratings do not consistently capture the sentiment in text reviews; on average, solo travelers' text reviews are perceived as more negative than the star ratings they assigned, whether they travel for business or pleasure. We then analyzed over 137,000 reviews from TripAdvisor and Venere and found that a co-occurrence network approach naturally divides the text of reviews into hotel aspects. We used this result to measure the importance of hotel aspects across various traveler modes and purposes and identified significant differences in their preferences. These findings underscore the need for personalized marketing and services, highlighting the role of trip mode in shaping online review sentiment and traveler satisfaction. [ABSTRACT FROM AUTHOR]

Travelers’ perceptions of hotels and their aspects have been the focus of much research and are often studied by analyzing consumers’ online reviews. Yet, little attention has been given to the effect of the trip mode, i.e., whether the person travels alone or with others, on travelers’ preferences as sentiment. Here, we study the influence of the trip mode and purpose using a mixed-methods approach. We conducted a user study to evaluate the perceptions of reviews across trip modes and found that star ratings do not consistently capture the sentiment in text reviews; on average, solo travelers’ text reviews are perceived as more negative than the star ratings they assigned, whether they travel for business or pleasure. We then analyzed over 137,000 reviews from TripAdvisor and Venere and found that a co-occurrence network approach naturally divides the text of reviews into hotel aspects. We used this result to measure the importance of hotel aspects across various traveler modes and purposes and identified significant differences in their preferences. These findings underscore the need for personalized marketing and services, highlighting the role of trip mode in shaping online review sentiment and traveler satisfaction.

The paper delves into social media mining in the context of medical education programs in the information age. It explores the adaptability of Social Media Analytics (SMA) apps within the structure of online courses in medicine and proposes a conceptual framework for a learning process. This process includes practical exercises based on search and social media mining in the healthcare industry, relying on technology solutions. An online course, "Managing Digital Content for Health Professionals", was developed at I.M. Sechenov First Moscow State Medical University to expand the understanding of digital content management processes, the specific details of in-depth social media analysis, and transforming social data into valuable knowledge for health professionals. The study group consisted of 108 participants. Throughout the course, students were tasked to ascertaining the effects gained during practical training and evaluating them. Participants identified the key professional and socio-personal effects of the practical training. Following the evaluation of the online apps' feature sets, the authors concluded that social media analytics requires a comprehensive approach, the synergy of digital tools, a strategy for adapting the mining to the field of expertise, and the paradigm of data synthesis and use. [ABSTRACT FROM AUTHOR]

The recognition of eye disorders has the potential to reduce blindness in people. The need for a procedural method is important to boost the overall recognition process. Although the identification of certain disease symptoms is crucial to an early diagnosis, this study proposed a procedural mechanism to predict eye diseases on the Twitter platform using users' sentiments embedded in their social media data. Glaucoma was investigated as one example of various eye diseases. Themes related to glaucoma were extracted using Latent Dirichlet Allocation. Subsequently, association rules mining was employed to identify disease-related symptoms within each theme. Our results showed that certain emotions, such as fear and sadness emotions, were highly associated with glaucoma messages. The findings revealed that emotion-related features have a significant impact on improving the prediction process of glaucoma in patients. As a result, this study proposes a low-cost procedural mechanism for the early-stage detection of eye disorders using microblogs data. The proposed approach can advance current efforts toward developing clinical decision support systems capable of detecting diseases online. [ABSTRACT FROM AUTHOR]

The recent surge in the usage of e-cigarettes amongst youth has highlighted a long-standing societal crisis. To assist public health agencies in policymaking, past research often employed traditional survey-based methods to understand youth behavior, which suffer from response biases and scalability, are time-consuming, and their findings often lag the fast-changing public behavior. Our study fills this gap by using social media as a complementary data source to understand user intentions for vape usage at a large scale, thus, providing an alternative to traditional survey-based methods. In this paper, we propose a novel user intent mining framework under the guidance of social cognitive theory for health behavioral interventions that helps study user intentions across different social media platforms. We then employ this framework to investigate the feasibility of automated intent mining on social media by formulating a multi-class classification task, employing machine learning algorithms to classify a social media message across relevant intent classes: Accusational, Anecdotal, Informational, Justificational and Promotional. The analyses indicate that Accusational tweets and Anecdotal messages were most prevalent on X/Twitter and Reddit respectively. We further provide novel insights on the conversational context using topic modeling analysis and psychometric analysis consequently, informing intervention designs and assisting health analysts. [ABSTRACT FROM AUTHOR]

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The rapid proliferation of social media has expanded its use across diverse domains, including commerce, business promotion, political messaging, education, and entertainment. However, this upsurge in social media activity has also attracted malevolent actors who exploit these platforms for illicit activities. This research, as discussed in the paper, is dedicated to discerning the user and societal attributes essential for the identification of fraudulent reviews, misinformation, and rumors disseminated on social media channels. Moreover, the study offers insights into the potential applicability of this research for recognizing illicit user cohorts, analyzing the influence of political ideologies, and assessing the impact of military users aligned with specific ideologies. The paper additionally underscores the significance of intrusion detection mechanisms on social media and introduces a deep neural network-based model for the identification of counterfeit profiles, as well as distinguishing between active and inactive profiles in the realm of social media. [ABSTRACT FROM AUTHOR]

Abstract Nowadays, social networks have taken on an irreplaceable role as sources of information. Millions of people use them daily to find out about the issues of the moment. This success has meant that the amount of content present in social networks is unmanageable and, in many cases, fake or non-credible. Therefore, a correct pre-processing of the data is necessary if we want to obtain knowledge and value from these data sets. In this paper, we propose a new data pre-processing technique based on Big Data that seeks to solve two of the key concepts of the Big Data paradigm, data validity and credibility of the data and volume. The system is a Spark-based filter that allows us to flexibly select credible users related to a given topic under analysis, reducing the volume of data and keeping only valid data for the problem under study. The proposed system uses the power of word embeddings in conjunction with other text mining and natural language processing techniques. The system has been validated using three real-world use cases.

2024 is a political year in Indonesia as it marks the presidential general election. The proliferation of survey institutions attempting to capture the electability levels of each candidate may not invariably yield accurate results, as evidenced by the events of the 2016 United States Presidential election. The loyal support creates tight competition and a narrow margin in electability levels among the three contending candidates. Opinion mining on social media offers an alternative that addresses the challenges often encountered when measuring electability using traditional survey methods. This study aims to build entitylevel sentiment classifiers as a new approach for predicting electability of presidential candidates based on citizen support on social media Twitter within the framework of the CRISP-DM model. The study compares 9 different algorithms with 3 vectorization techniques. Evaluation measurement with 4 metrics: accuracy, precision, recall and f1-score is performed. As a result, TF-IDF 3-gram Random Forest achieves the highest fiscore 0.84486. The selected model is then employed to measure the presidential candidates' electability levels over time. Besides streamlining the process, social media's opinion mining enables the candidates and their constituents to monitor electability levels affordably in real-time and on-demand manner, which is advantageous compared to traditional surveys. [ABSTRACT FROM AUTHOR]

Social media has a profound impact on the formation of end-users' social, economic, and political views. Unfortunately, some advertisement agencies and miscreants use fake and misleading reviews to influence people's opinions on their political and business interests. These fake reviews are often posted using fake profiles to conceal the identity of the perpetrator. Such fake reviews are spread on social media platforms through Sockpuppets and Crowdturfing based fake accounts. This paper proposes the Communal Influence Propagation Framework (CIPF), which identifies fake accounts by analysing the essential features set from individual user profile, linguistic, and group profiles (network) feature in userspace. Initially, CIPF scrutinises individual user profile features, group profiles (network) features, and linguistic features to generate the feature vector of the userspace. The CIPF framework then uses the Influence, Homophily and Balance theory of Social Media Mining (SMM) to enrich the malicious user space as an influential index. Additionally, the Jaccard coefficient evaluates the similarity index vector over the influential negative node, identifies Sockpuppet nodes, and generates a negative propagation belonging matrix. The CIPF framework amalgamates influence-based two-tier verification of malicious nodes, the first being the Sockpuppet Detection Phase (IB-SPD) and the second being the Convolutional Neural Network (CNN) based influence-based Crowdturfing Community (IB-CFC). The CIPF framework is evaluated based on the classification performance of Sockpuppet nodes, modularity, and normalised mutual information of the structured crowdturfing community. As a result, the CIPF achieves an approximate 98% accuracy for classifying Sockpuppet nodes and a structured 0.94 modular and 0.91 informative crowdturfing community. [ABSTRACT FROM AUTHOR]

The outbreak of the novel coronavirus, COVID-19, has become one of the most severe pandemics in human history. In this paper, we propose to leverage social media users as social sensors to simultaneously predict the pandemic trends and suggest potential risk factors for public health experts to understand spread situations and recommend proper interventions. More precisely, we develop novel deep learning models to recognize important entities and their relations over time, thereby establishing dynamic heterogeneous graphs to describe the observations of social media users. A dynamic graph neural network model can then forecast the trends (e.g. newly diagnosed cases and death rates) and identify high-risk events from social media. Based on the proposed computational method, we also develop a web-based system for domain experts without any computer science background to easily interact with. We conduct extensive experiments on large-scale datasets of COVID-19 related tweets provided by Twitter, which show that our method can precisely predict the new cases and death rates. We also demonstrate the robustness of our web-based pandemic surveillance system and its ability to retrieve essential knowledge and derive accurate predictions across a variety of circumstances. Our system is also available at http://scaiweb.cs.ucla.edu/covidsurveiller/. This article is part of the theme issue 'Data science approachs to infectious disease surveillance'.

Sentiment analysis measures user experience on social media. With the emergence of pre-trained models, text classification tasks have become homogeneous, without a significant improvement in accuracy. Therefore, we present a hybrid model called PLASA to classify the sentiment polarity of short comments, particularly barrages. PLASA introduces a collaborative attention module that integrates information about relative position and knowledge from summarized lexicons to better adjust the relationship between word representations. Our model is evaluated using three new curated sentiment analysis datasets: SentiTikTok-2023 (4613 items), SentiBilibili-2023 (7755 items), and SentiWeibo-2023 (5614 items). Although the comment length varies across datasets, all maintain a consistent punctuation percentage at approximately 13%. Consequently, PLASA with the optimal combination demonstrates notable performance improvements compared to both the baseline and commonly used models. It achieves micro-F1 scores of 93.94%, 90.34%, and 88.79% on the respective datasets. We also observed that the representation capacity of the pre-trained model decreases as the text length increases. Moreover, the proposed collaborative attention module effectively addresses this limitation, as confirmed by our ablation study.

How can we know what stakeholders think and feel about brands in real time and over time? Most brand reputation measures are at the aggregate level (e.g., the Interbrand "Best Global Brands" list) or rely on customer brand perception surveys on a periodical basis (e.g., the Y&R Brand Asset Valuator). To answer this question, brand reputation measures must capture the voice of the stakeholders (not just ratings on brand attributes), reflect important brand events in real time, and connect to a brand's financial value to the firm. This article develops a new social media–based brand reputation tracker by mining Twitter comments for the world's top 100 brands using Rust–Zeithaml–Lemon's value–brand–relationship framework, on a weekly, monthly, and quarterly basis. The article demonstrates that brand reputation can be monitored in real time and longitudinally, managed by leveraging the reciprocal and virtuous relationships between the drivers, and connected to firm financial performance. The resulting measures are housed in an online longitudinal database and may be accessed by brand reputation researchers. [ABSTRACT FROM AUTHOR]

Alter ego is a condition of someone who creates a new character with a conscious state. Original character role play is a game to create new imaginary characters that is used as research material for identification alter ego accounts. The negative effects of playing alter ego are stress, depression, and multiple personalities. Current research only focuses on the phenomenon and impacts of a role-playing game. We propose a new method to detect accounts of alter ego players in social media, especially Twitter. We develop an application to analyze the characteristics of alter ego accounts. Psychologists can use this application to discover the characteristics of alter ego accounts that are useful for analyzing personality so that the results can be used to appropriately handle alter ego players. Most user profiles, tweets, and platforms are used to detect account Twitter. This research proposes a new method using bio features as input data. We crawled and collected 565 bios from Twitter for one month. We observe the data to search for unique words and collect them into a classification dictionary. In this research, we use the cosine similarity method because this method is popular for detecting text and has a good performance in many cases. This research could identify alter ego accounts and other types of Twitter accounts. From the detection results of alter ego accounts, it is possible to analyze the characteristics of Twitter accounts. We use a sampling technique that takes 30% of the data as testing data. According to the results of the experiment cosine similarity obtained an accuracy of 0.95.

This document is an editorial from the journal "Frontiers in Research Metrics & Analytics" titled "Text mining-based mental health research." It discusses the use of text mining and data analytics in the field of mental health research. The editorial highlights the importance of analyzing scientific literature datasets, social media user-generated content datasets, and the Bipolar Reddit community to gain insights into mental health conditions. The authors emphasize the need for interdisciplinary collaboration and technological advancements to address mental health issues effectively. They also acknowledge the potential challenges and risks involved in analyzing sentiments and topics related to mental health on social media platforms. [Extracted from the article]

In the age of digital economy, customers actively share their experiences and issues about products via online product reviews. Mining potential product improvement ideas from customer needs could provide valuable insights into new functionality expected by the markets. Numerous studies have attempted to identify customer needs using these reviews, but they paid less attention to the customer’s specific context in which the product was used. This study provides a novel approach for identifying customer needs based on both context information and product functions of target products. The context information and product functions are derived from online product reviews through linguistic pattern mining, whereby the customer needs are determined by the combination of extracted context information and product functions using a semantic embedding method and a clustering approach. A case study on the Amazon-Echo series was conducted to verify the applicability of the proposed approach. Consequently, we identified 1430 different customer needs, which could be used as an input for improving product design. This study is one of the first attempts to integrate context information for identifying customer needs. The proposed approach can be useful in the idea creation process for future product planning and is expected to add new empirical perspective for the e-commerce industry.

Community structure over social media (SM) is the collaborative group of globally spread users with identical characteristics and ideologies. The collective features of SM are inherent with both the implicit and explicit nature of end-users. This paper presents an analytical and methodological community detection framework to bind passive users' implicit and explicit nature after scrutinizing graphical data to identify seed nodes and communities. Moreover, this work provides the concept of the unsupervised machine learning approach over the graphical perspective of SM to identify the trade-off between similarity of nodes attributes and density of connections for social theories. Subsequently, this paper evaluates a comprehensive analysis of the benchmark community detection algorithm (CDA) Label Propagation Algorithm (LPA), Clique Percolation Method (CPM), Democratic Estimate of the Modular Organization of a Network (DEMON) and Non-Negative Matrix Factorization (NMF). The evaluation has been carried out over modularity and normalized mutual information of resultant structured community on six real-time SM data set. The performance of benchmark CDAs is significantly increased after incorporating social theories. NMF, DEMON, CPM and LPA gained the highest improvement over Zachary's Karate Club data sets, i.e. approximate 26.91%, 21.68%, 18.79%, 19.96%, respectively. [ABSTRACT FROM AUTHOR]

Analyzing community response has always played an important role in marketing and development of various products ranging from services to manufacturing. Analyzing the responses and interpreting them helps improve the quality of the product. This task is traditionally done by community managers using surveys and focus groups. One other hands-off solution would be using the technology to analyze sentiments from users' digital interactions in various online communities. Sentiment Analysis is the use of Natural Language Processing to process text to identify and retrieve sentiments behind the textual data. Sentiment Analysis can be approached either by supervised classification where a machine learning model is trained using labeled data or lexicon-based unsupervised analysis where a sentiment dictionary is used to find the overall sentiment. The idea is to build a system which presents community sentiment results on discussion threads from reddit on demand with the end user not to concern about data processing. We propose a streamlined pipeline architecture for a web application with several closely connected components in the backend that allows users to simply send the reddit discussion thread's URL, which the backend receives and scrapes the comments using the Reddit API, creates a dataset, cleans it and performs sentiment analysis using a RNNs. We used a Long Short-Term Memory (LSTM) a variant of RNN like Bidirectional RNNs and Multi-layered RNNs are also used to get better results. [ABSTRACT FROM AUTHOR]

Measuring disaster resilience from the perspective of long-term recovery ability is important for the planning and construction of urban sustainability, whereas short-term resilient recovery can better reflect a city's ability to recover quickly after a disaster occurs. This study proposes an analytical framework for urban disaster recovery and resilience based on social media data that can analyze short-term disaster recovery and assess disaster resilience from the perspectives of infrastructure and people's psychological states. We consider the downpour in Henan, China, in July 2021. The results show that (1) social media data can effectively reflect short-term disaster recovery, (2) disaster resilience can be assessed using social media data combined with rainfall and damage data, and (3) the framework can quantitatively reflect the differences in disaster recovery and resilience across regions. The findings can facilitate better decision-making in disaster emergency management for precise and effective post-disaster reconstruction and psychological intervention, and provide references for cities to improve disaster resilience. [ABSTRACT FROM AUTHOR]

As COVID-19 ravages the world, social media analytics could augment traditional surveys in assessing how the pandemic evolves and capturing consumer chatter that could help healthcare agencies in addressing it. This typically involves mining disclosure events that mention testing positive for the disease or discussions surrounding perceptions and beliefs in preventative or treatment options. The 2020 shared task on COVID-19 event extraction (conducted as part of the W-NUT workshop during the EMNLP conference) introduced a new Twitter dataset for benchmarking event extraction from COVID-19 tweets. In this paper, we cast the problem of event extraction as extractive question answering using recent advances in continuous prompting in language models. On the shared task test dataset, our approach leads to over 5% absolute micro-averaged F1-score improvement over prior best results, across all COVID-19 event slots. Our ablation study shows that continuous prompts have a major impact on the eventual performance. [ABSTRACT FROM AUTHOR]

The ability to accelerate the discovery of product innovation ideas is critical for manufacturers with short life cycles, such as smartphones. Companies with short product lifespans require innovative strategies to improve their products based on customer needs. Three-dimensional concurrent engineering (3DCE) has the potential to speed up the process of developing new products. On the other side, the availability of online data, such as social media and intellectual property, has the potential to identify and prioritize product, technology, and supplier opportunities in a cost-effective, quick, and real-time manner. This study proposes an online data-driven concurrent product, process, and supply chain design (3DCE) for linking customer requirements to potential technology and supplier opportunities in early-stage new product development. Three case studies on the leading smartphone industry demonstrate the proposed method's feasibility and effectiveness. To identify customer requirements, opinions from social media data are collected. Product opportunities are developed using latent Dirichlet allocation to identify product topics and sentiment analysis to assess satisfaction levels. Then, a novel approach by association rule mining is developed to mine specific customer requirements for each promising topic. In the 3DCE approach, the potential technology and supplier opportunities are tailored to specific customer requirements based on intellectual property mining. Finally, this study discovers improvement ideas and potential alternative solutions for product development with an unsupervised approach. The proposed method has also been validated and aligns with successful innovation products by Apple, Samsung, and Huawei.

Purpose: This study aimed to examine the mental health information entities and associations between the biomedical, psychological and social domains of bipolar disorder (BD) by analyzing social media data and scientific literature. Design/methodology/approach: Reddit posts and full-text papers from PubMed Central (PMC) were collected. The text analysis was used to create a psychological dictionary. The text mining tools were applied to extract BD entities and their relationships in the datasets using a dictionary- and rule-based approach. Lastly, social network analysis and visualization were employed to view the associations. Findings: Mental health information on the drug side effects entity was detected frequently in both datasets. In the affective category, the most frequent entities were "depressed" and "severe" in the social media and PMC data, respectively. The social and personal concerns entities that related to friends, family, self-attitude and economy were found repeatedly in the Reddit data. The relationships between the biomedical and psychological processes, "afraid" and "Lithium" and "schizophrenia" and "suicidal," were identified often in the social media and PMC data, respectively. Originality/value: Mental health information has been increasingly sought-after, and BD is a mental illness with complicated factors in the clinical picture. This paper has made an original contribution to comprehending the biological, psychological and social factors of BD. Importantly, these results have highlighted the benefit of mental health informatics that can be analyzed in the laboratory and social media domains. [ABSTRACT FROM AUTHOR]

Recommender systems nowadays are commonly deployed in e-commerce platforms to help customers making purchase decisions. Dynamic recommender considers not only static user-item interaction data, but the temporal information at the time of recommendation. Previous researches have suggested to incorporate social media as the temporal information in dynamic neural recommenders after transforming them into embeddings. While such an approach can potentially improve recommendation performance, the effectiveness is difficult to explain. In this article, we propose an explainable method to integrate social media in a dynamic neural recommender. Our method applies association rule mining, which can generate human-understandable behavior patterns from social media and e-commerce platforms. With real-world social media and e-commerce data, we show that the integration can improve accuracy by up to 14% while using the same data. Moreover, we can explain the positive cases by examining relevant association rules. [ABSTRACT FROM AUTHOR]

Topic trending is a popular research topic in recent years since, there are massive participations in social web sites, countless number of updates, news, opinions and product reviews are being constantly posted every day. The identification of popular topics discussed or posted on social media platforms is becoming more important as the new knowledge can be extracted from these findings. In this work, a novel method is proposed to extract popular topics from social media and determine the topic trending based on timeline using the Apriori algorithm. The approach uses Twitter's tweets as the dataset. The data is then pre-processed by undergoing several processes that include stop words removal, stemming, tokenization and Term Frequency-Inverse Document Frequency (TF - IDF) weighting. The k-means clustering is then performed to cluster each data that consists of processed keywords and collected every day. The popular topics will be then extracted from the clusters and the topic trends will be determined based on the observed frequent patterns and correlation between keywords by using the association rules. The performance of the proposed method is evaluated based on the similarity of the results with the current trends obtained from the Twitter site. The result from the findings shows that the proposed method is able to produce more enriched trends that are similar to current initial trends. [ABSTRACT FROM AUTHOR]

The incursion of social media in our lives has been much accentuated in the last decade. This has led to a multiplication of data mining tools aimed at obtaining knowledge from these data sources. One of the greatest challenges in this area is to be able to obtain this knowledge without the need for training processes, which requires structured information and pre-labelled datasets. This is where unsupervised data mining techniques come in. These techniques can obtain value from these unstructured and unlabelled data, providing very interesting solutions to enhance the decision-making process. In this paper, we first address the problem of social media mining, as well as the need for unsupervised techniques, in particular association rules, for its treatment. We follow with a broad overview of the applications of association rules in the domain of social media mining, specifically, their application to the problems of mining textual entities, such as tweets. We also focus on the strengths and weaknesses of using association rules for solving different tasks in textual social media. Finally, the paper provides a perspective overview of the challenges that association rules must face in the next decade within the field of social media mining. [ABSTRACT FROM AUTHOR]

Multimodal classification of social media is used to classify data from different modalities into different categories, which is essential for understanding user behavior on the web. In this paper, we focus on classifying image-text pairs, specifically user-generated content on social media. Recently, the transformer network, a kind of self-attention network, has been widely studied in the disciplines of visual computing and language processing. In the attention mechanism, positive correlation is considered. However, multimedia content posted on social media is diverse. Images and text are not always consistent, and contrary information is also helpful for representation. Therefore, it is equally important to detect conflicts based on negative or inverse attention. Inspired by the attention mechanism, we propose a novel model, namely Crossmodal Bipolar Attention Network (CBAN). Different from existing positive dot-product and additive attention mechanisms, we propose a bipolar attention mechanism, which fuses visual and textual information through their direct and inverse semantic relationships to classify multimodal data. We conducted experiments on multiple multimodal classification data sets, for performing sentiment analysis, sarcasm detection, crisis categorization and hate-speech detection. Experimental results show that our proposed CBAN consistently outperforms state-of-the-art methods in all classification tasks. [ABSTRACT FROM AUTHOR]