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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.

Patients advise their peers on how to cope with their illness in daily life on online support groups. To date, no efforts have been made to automatically extract recommended coping strategies from online patient discussion groups. We introduce this new task, which poses a number of challenges including complex, long entities, a large long-tailed label space, and cross-document relations. We present an initial ontology for coping strategies as a starting point for future research on coping strategies, and the first end-to-end pipeline for extracting coping strategies for side effects. We also compared two possible computational solutions for this novel and highly challenging task; multi-label classification and named entity recognition (NER) with entity linking (EL). We evaluated our methods on the discussion forum from the Facebook group of the worldwide patient support organization 'GIST support international' (GSI); GIST support international donated the data to us. We found that coping strategy extraction is difficult and both methods attain limited performance (measured with F 1 score) on held out test sets; multi-label classification outperforms NER+EL (F 1 =0.220 vs F 1 =0.155). An inspection of the multi-label classification output revealed that for some of the incorrect predictions, the reference label is close to the predicted label in the ontology (e.g. the predicted label 'juice' instead of the more specific reference label 'grapefruit juice'). Performance increased to F 1 =0.498 when we evaluated at a coarser level of the ontology. We conclude that our pipeline can be used in a semi-automatic setting, in interaction with domain experts to discover coping strategies for side effects from a patient forum. For example, we found that patients recommend ginger tea for nausea and magnesium and potassium supplements for cramps. This information can be used as input for patient surveys or clinical studies., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 The Author(s). Published by Elsevier Inc. All rights reserved.)

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'.

The incorporation of patients' perspectives into drug discovery and development has become critically important from the viewpoint of accounting for modern-day business dynamics. There is a trend among patients to narrate their disease experiences on social media. The insights gained by analyzing the data pertaining to such social-media posts could be leveraged to support patient-centered drug development. Manual analysis of these data is nearly impossible, but artificial intelligence enables automated and cost-effective processing, also referred as social media mining (SMM). This paper discusses the fundamental SMM methods along with several relevant drug-development use cases., (Copyright © 2021 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Objective: Research on pharmacovigilance from social media data has focused on mining adverse drug events (ADEs) using annotated datasets, with publications generally focusing on 1 of 3 tasks: ADE classification, named entity recognition for identifying the span of ADE mentions, and ADE mention normalization to standardized terminologies. While the common goal of such systems is to detect ADE signals that can be used to inform public policy, it has been impeded largely by limited end-to-end solutions for large-scale analysis of social media reports for different drugs., Materials and Methods: We present a dataset for training and evaluation of ADE pipelines where the ADE distribution is closer to the average 'natural balance' with ADEs present in about 7% of the tweets. The deep learning architecture involves an ADE extraction pipeline with individual components for all 3 tasks., Results: The system presented achieved state-of-the-art performance on comparable datasets and scored a classification performance of F1 = 0.63, span extraction performance of F1 = 0.44 and an end-to-end entity resolution performance of F1 = 0.34 on the presented dataset., Discussion: The performance of the models continues to highlight multiple challenges when deploying pharmacovigilance systems that use social media data. We discuss the implications of such models in the downstream tasks of signal detection and suggest future enhancements., Conclusion: Mining ADEs from Twitter posts using a pipeline architecture requires the different components to be trained and tuned based on input data imbalance in order to ensure optimal performance on the end-to-end resolution task., (© The Author(s) 2021. Published by Oxford University Press on behalf of the American Medical Informatics Association.)

Our goal is to summarise and aggregate information from social media regarding the symptoms of a disease, the drugs used and the treatment effects both positive and negative. To achieve this we first apply a supervised machine learning method to automatically extract medical concepts from natural language text. In an environment such as social media, where new data is continuously streamed, we need a methodology that will allow us to continuously train with the new data. To attain such incremental re-training, a semi-supervised methodology is developed, which is capable of learning new concepts from a small set of labelled data together with the much larger set of unlabelled data. The semi-supervised methodology deploys a conditional random field (CRF) as the base-line training algorithm for extracting medical concepts. The methodology iteratively augments to the training set sentences having high confidence, and adds terms to existing dictionaries to be used as features with the base-line model for further classification. Our empirical results show that the base-line CRF performs strongly across a range of different dictionary and training sizes; when the base-line is built with the full training data the F 1 score reaches the range 84%-90%. Moreover, we show that the semi-supervised method produces a mild but significant improvement over the base-line. We also discuss the significance of the potential improvement of the semi-supervised methodology and found that it is significantly more accurate in most cases than the underlying base-line model., (Crown Copyright © 2020. Published by Elsevier Inc. All rights reserved.)

Background: Although birth defects are the leading cause of infant mortality in the United States, methods for observing human pregnancies with birth defect outcomes are limited., Objective: The primary objectives of this study were (i) to assess whether rare health-related events-in this case, birth defects-are reported on social media, (ii) to design and deploy a natural language processing (NLP) approach for collecting such sparse data from social media, and (iii) to utilize the collected data to discover a cohort of women whose pregnancies with birth defect outcomes could be observed on social media for epidemiological analysis., Methods: To assess whether birth defects are mentioned on social media, we mined 432 million tweets posted by 112,647 users who were automatically identified via their public announcements of pregnancy on Twitter. To retrieve tweets that mention birth defects, we developed a rule-based, bootstrapping approach, which relies on a lexicon, lexical variants generated from the lexicon entries, regular expressions, post-processing, and manual analysis guided by distributional properties. To identify users whose pregnancies with birth defect outcomes could be observed for epidemiological analysis, inclusion criteria were (i) tweets indicating that the user's child has a birth defect, and (ii) accessibility to the user's tweets during pregnancy. We conducted a semi-automatic evaluation to estimate the recall of the tweet-collection approach, and performed a preliminary assessment of the prevalence of selected birth defects among the pregnancy cohort derived from Twitter., Results: We manually annotated 16,822 retrieved tweets, distinguishing tweets indicating that the user's child has a birth defect (true positives) from tweets that merely mention birth defects (false positives). Inter-annotator agreement was substantial: κ = 0.79 (Cohen's kappa). Analyzing the timelines of the 646 users whose tweets were true positives resulted in the discovery of 195 users that met the inclusion criteria. Congenital heart defects are the most common type of birth defect reported on Twitter, consistent with findings in the general population. Based on an evaluation of 4169 tweets retrieved using alternative text mining methods, the recall of the tweet-collection approach was 0.95., Conclusions: Our contributions include (i) evidence that rare health-related events are indeed reported on Twitter, (ii) a generalizable, systematic NLP approach for collecting sparse tweets, (iii) a semi-automatic method to identify undetected tweets (false negatives), and (iv) a collection of publicly available tweets by pregnant users with birth defect outcomes, which could be used for future epidemiological analysis. In future work, the annotated tweets could be used to train machine learning algorithms to automatically identify users reporting birth defect outcomes, enabling the large-scale use of social media mining as a complementary method for such epidemiological research., (Copyright © 2018 The Authors. Published by Elsevier Inc. All rights reserved.)

Background: It is becoming increasingly common for individuals and organizations to use social media platforms such as Facebook. These are being used for a wide variety of purposes including disseminating, discussing and seeking health related information. U.S. Federal health agencies are leveraging these platforms to 'engage' social media users to read, spread, promote and encourage health related discussions. However, different agencies and their communications get varying levels of engagement. In this study we use statistical models to identify factors that associate with engagement., Methods: We analyze over 45,000 Facebook posts from 72 Facebook accounts belonging to 24 health agencies. Account usage, user activity, sentiment and content of these posts are studied. We use the hurdle regression model to identify factors associated with the level of engagement and Cox proportional hazards model to identify factors associated with duration of engagement., Results: In our analysis we find that agencies and accounts vary widely in their usage of social media and activity they generate. Statistical analysis shows, for instance, that Facebook posts with more visual cues such as photos or videos or those which express positive sentiment generate more engagement. We further find that posts on certain topics such as occupation or organizations negatively affect the duration of engagement., Conclusions: We present the first comprehensive analyses of engagement with U.S. Federal health agencies on Facebook. In addition, we briefly compare and contrast findings from this study to our earlier study with similar focus but on Twitter to show the robustness of our methods.

Objective: Social media is becoming increasingly popular as a platform for sharing personal health-related information. This information can be utilized for public health monitoring tasks, particularly for pharmacovigilance, via the use of natural language processing (NLP) techniques. However, the language in social media is highly informal, and user-expressed medical concepts are often nontechnical, descriptive, and challenging to extract. There has been limited progress in addressing these challenges, and thus far, advanced machine learning-based NLP techniques have been underutilized. Our objective is to design a machine learning-based approach to extract mentions of adverse drug reactions (ADRs) from highly informal text in social media., Methods: We introduce ADRMine, a machine learning-based concept extraction system that uses conditional random fields (CRFs). ADRMine utilizes a variety of features, including a novel feature for modeling words' semantic similarities. The similarities are modeled by clustering words based on unsupervised, pretrained word representation vectors (embeddings) generated from unlabeled user posts in social media using a deep learning technique., Results: ADRMine outperforms several strong baseline systems in the ADR extraction task by achieving an F-measure of 0.82. Feature analysis demonstrates that the proposed word cluster features significantly improve extraction performance., Conclusion: It is possible to extract complex medical concepts, with relatively high performance, from informal, user-generated content. Our approach is particularly scalable, suitable for social media mining, as it relies on large volumes of unlabeled data, thus diminishing the need for large, annotated training data sets., (© The Author 2015. Published by Oxford University Press on behalf of the American Medical Informatics Association.)

Objectives: Adverse drug reactions (ADRs) are believed to be a leading cause of death in the world. Pharmacovigilance systems are aimed at early detection of ADRs. With the popularity of social media, Web forums and discussion boards become important sources of data for consumers to share their drug use experience, as a result may provide useful information on drugs and their adverse reactions. In this study, we propose an automated ADR related posts filtering mechanism using text classification methods. In real-life settings, ADR related messages are highly distributed in social media, while non-ADR related messages are unspecific and topically diverse. It is expensive to manually label a large amount of ADR related messages (positive examples) and non-ADR related messages (negative examples) to train classification systems. To mitigate this challenge, we examine the use of a partially supervised learning classification method to automate the process., Methods: We propose a novel pharmacovigilance system leveraging a Latent Dirichlet Allocation modeling module and a partially supervised classification approach. We select drugs with more than 500 threads of discussion, and collect all the original posts and comments of these drugs using an automatic Web spidering program as the text corpus. Various classifiers were trained by varying the number of positive examples and the number of topics. The trained classifiers were applied to 3000 posts published over 60 days. Top-ranked posts from each classifier were pooled and the resulting set of 300 posts was reviewed by a domain expert to evaluate the classifiers., Results: Compare to the alternative approaches using supervised learning methods and three general purpose partially supervised learning methods, our approach performs significantly better in terms of precision, recall, and the F measure (the harmonic mean of precision and recall), based on a computational experiment using online discussion threads from Medhelp., Conclusions: Our design provides satisfactory performance in identifying ADR related posts for post-marketing drug surveillance. The overall design of our system also points out a potentially fruitful direction for building other early warning systems that need to filter big data from social media networks., (Copyright © 2015 Elsevier Inc. All rights reserved.)

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]

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]

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'.

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]

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]

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]

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]

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]

Background: Since the beginning of the COVID-19 pandemic, over 480 million people have been infected and more than 6 million people have died from COVID-19 worldwide. In some patients with acute COVID-19, symptoms manifest over a longer period, which is also called "long-COVID." Unmet medical needs related to long-COVID are high, since there are no treatments approved. Patients experiment with various medications and supplements hoping to alleviate their suffering. They often share their experiences on social media. Objective: The aim of this study was to explore the feasibility of social media mining methods to extract important compounds from the perspective of patients. The goal is to provide an overview of different medication strategies and important agents mentioned in Reddit users' self-reports to support hypothesis generation for drug repurposing, by incorporating patients' experiences. Methods: We used named-entity recognition to extract substances representing medications or supplements used to treat long-COVID from almost 70,000 posts on the "/r/covidlonghaulers" subreddit. We analyzed substances by frequency, co-occurrences, and network analysis to identify important substances and substance clusters. Results: The named-entity recognition algorithm achieved an F1 score of 0.67. A total of 28,447 substance entities and 5789 word co-occurrence pairs were extracted. "Histamine antagonists," "famotidine," "magnesium," "vitamins," and "steroids" were the most frequently mentioned substances. Network analysis revealed three clusters of substances, indicating certain medication patterns. Conclusions: This feasibility study indicates that network analysis can be used to characterize the medication strategies discussed in social media. Comparison with existing literature shows that this approach identifies substances that are promising candidates for drug repurposing, such as antihistamines, steroids, or antidepressants. In the context of a pandemic, the proposed method could be used to support drug repurposing hypothesis development by prioritizing substances that are important to users. [ABSTRACT FROM AUTHOR]

With the rise of social media, brand communication has undergone tremendous changes. In particular, small brands have been affected severely. Given this, this study investigated strategies for how small brands can survive in the digital age using a case study of Louise Kennedy, a representative Irish fashion designer brand. After examining a total of 2,899 tweets, the study finds a strategic mechanism for heritage branding on social media, and confirms the important role of cultural heritage in the success of small brands. As a result, the study extends prior studies on heritage branding to fashion marketing on social media. Also, it provides actionable insights for small brands to survive the social media firestorm. [ABSTRACT FROM AUTHOR]

Point-of-Interest recommendation is an efficient way to explore interesting unknown locations in social media mining of social networks. In order to solve the problem of sparse data and inaccuracy of single user model, we propose a User-City-Sequence Probabilistic Generation Model (UCSPGM) integrating a collective individual self-adaptive Markov model and the topic model. The collective individual self-adaptive Markov model consists of three parts such as the collective Markov model, the individual self-adaptive Markov model and the self-adaptive rank method. The former determines the topic sequence for all users in system and mines the behavioral patterns of users in a large environment. The later mines behavioral patterns for each user in a small environment. The last determines a self-adaptive-rank for each user in niche. We conduct a large amount of experiments to verify the effectiveness and efficiency of our method. [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. [ABSTRACT FROM AUTHOR]

Social media mining is the process of collecting large datasets from user-generated content and extracting and analyzing social media interactions to recognize meaningful patterns in individual and social behavior. Everyday, more contents related to social media are generated by social media users (e.g., Facebook, Twitter). As the components of big data continue to expand, the task of extracting useful information becomes critical. Topic extraction refers to the process of extracting main topics from the pool of news feed and a typical method to perform topic extraction is through clustering. Clustering defines or organizes a group of patterns or objects into clusters, allows high-dimensional data to be presented in an apprehensive fashion to humans. Although effective, the performance of the k-means clustering algorithm depends heavily on the initial centroids and the number of clusters, k. Recently, several effective supervised and unsupervised machine learning methods have been developed in the domain of topics extraction. However, less works have been conducted in applying multiobjective based algorithm for topic extraction. Most of these algorithms are not optimized, even if they are, they are only optimized by using a single objective method and may underperform when solving real-world problems which are typically multi-objectives in nature. This paper investigates the effects of using a multiobjective genetic algorithm (MOGA) based clustering technique to cluster texts for topic extraction which is designed based on the structure and purity of the clusters in order to determine the optimal initial centroids and the number of clusters, k. Then, the mapping percentages between the predefined and produced clusters are used to assess the performance of the proposed algorithm. The best mapping percentage of 62.7 obtained using the proposed algorithm when k = 15 is obtained to outperform the performance of the generic k-means. The top five most representative words from each cluster are also extracted and validated by computing the number of tweets related to the predefined tags. [ABSTRACT FROM AUTHOR]

This paper explores the public perception of economic measures implemented as a reaction to the COVID-19 pandemic in Poland in March–June 2020. A mixed-method approach was used to analyse big data coming from tweets and Facebook posts related to the mitigation measures to provide evidence for longitudinal trends, correlations, theme classification and perception. The online discussion oscillated around political and economic issues. The implementation of the anti-crisis measures triggered a barrage of criticism pointing out the shortcomings and ineffectiveness of the solutions. The revised relief legislation was accompanied by a wide-reaching informative campaign about the relief package, which decreased negative sentiment. The analysis also showed that with regard to online discussion about risk mitigation, social media users are more concerned about short-term economic and social effects rather than long-term effects of the pandemic. The findings have significant implications for the understanding of public sentiment related to the COVID-19 pandemic, economic attitudes and relief support implemented to fight the adverse effects of the pandemic. [ABSTRACT FROM AUTHOR]

Traffic safety culture has emerged as a significant factor in support of the recognition of existing traffic safety policies and programs and as a contextual variable to describe high-risk behaviors of drivers. However, it is an arduous task to understand people's beliefs and attitudes that collectively make up traffic safety-related influences. The growing acceptance of social media platforms such as Facebook and Twitter have spurred great interest in massive data collection and its use in conducting a comprehensive analysis of people's viewpoints on a particular topic. This study applied social media mining to shed light on traffic safety culture in the state of Washington. To this end, the researchers collected traffic safety–related tweets over the past 4 years in Washington based on a set of keywords. After cleaning and reprocessing, the collected tweets were used in sentiment analysis using Linguistic Inquiry and Word Count (LIWC) to measure the public's beliefs and attitudes toward the importance of traffic safety, possibility of zero fatalities, usefulness of traffic law enforcement, and six types of high-risk behaviors, including impaired driving, speeding, distracted driving, unrestrained vehicle occupants, teenage drivers, and older drivers. Next, the topic modeling technique was applied to discover important latent topics related to traffic safety culture. This research, which capitalizes on social media mining, overcomes the limitations of the conventional survey method, which are time-consuming and costly. The generated information may facilitate understanding of the barriers to preventing fatal traffic accidents in Washington and around the country and developing solutions to overcome them. [ABSTRACT FROM AUTHOR]

Coronavirus 2019, or COVID-19, is a contagious disease triggered by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). With origins in Wuhan, China, this disease has since spread globally, resulting in the ongoing 2019–2020 coronavirus pandemic. As of May 3, 2020, the Ministry of Health and Family Welfare confirmed a total of 39,980 positive COVID-19 cases and 1,301 deaths in India (more than 3.42 million positive COVID-19 cases resulting in more than 243,000 deaths worldwide). To flatten the curve, India has been locking down its country from March 24 to May 17, 2020. This study collected "COVID-19 in India" related tweets (totaling 410,643 tweets in English) from March 22 to April 21, 2020 to gauge the unknowns and contexts associated with public sentiments during the lockdown. This work contributes to the growing body of studies on COVID-19 social media mining by extracting emotions and sentiments over time, which could potentially shed some lights on the contexts of expressions during pandemic. [ABSTRACT FROM AUTHOR]

The imperative of well-being and improved quality of life in smart cities context can only be attained if the smart services, so central to the concept of smart cities, correspond with the needs, expectations and skills of cities' inhabitants. Considering that social media generate and/or open real-time entry points to vast amounts of data pertinent to well-being and quality of life, such as citizens' expectations, opinions, as well as to recent developments related to regulatory frameworks, debates, political decisions and policymaking, the big question is how to exploit the potential inherent in social media and use it to enhance the value added smart cities generate. Social mining is traditionally understood as the process of representing, analyzing, and extracting actionable patterns and trends from raw social media data. In the context of smart cities, this special issue focuses on how social media data, also potentially combined with other data, can be used to optimize the efficiency of city operations and services, and thereby contribute more efficiently to citizens' well-being and quality of life. [ABSTRACT FROM AUTHOR]

Social media stores a significant amount of information which can be used for extraction of specific knowledge. A variety of topics that arise there concerns a lot of everyday life aspects, including urban-related problems. In this work, we demonstrate the way of using the texts from social media on the topic of housing and utility problems, such as litter on the streets, graffiti on a public building or noisy neighbours. Our aim is to develop an approach based on machine learning to automatically filter such citizen messages and classify them into several categories. To achieve this, we solve the classification problem with an almost unlimited number of negative categories using the One-Class approach and combine data from several resources to construct proper text embedding by combining results from the guided topic model and deep neural pretrained BERT method. Comparison with statistics taken from the official site indicates that the distributions of posts on each problem category are similar for districts of Saint-Petersburg [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Purpose: Food plays an important role in every culture around the world. Recently, cuisine preference analysis has become a popular research topic. However, most of these studies are conducted through questionnaires and interviews, which are highly limited by the time, cost and scope of data collection, especially when facing large-scale survey studies. Some researchers have, therefore, attempted to mine cuisine preferences based on online recipes, while this approach cannot reveal food preference from people's perspective. Today, people are sharing what they eat on social media platforms by posting reviews about the meal, reciting the names of appetizers or entrees, and photographing as well. Such large amount of user-generated contents (UGC) has potential to indicate people's preferences over different cuisines. Accordingly, the purpose of this paper is to explore Chinese cuisine preferences among online users of social media. Design/methodology/approach: Based on both UGC and online recipes, the authors first investigated the cuisine preference distribution in different regions. Then, dish preference similarity between regions was calculated and few geographic factors were identified, which might lead to such regional similarity appeared in our study. By applying hierarchical clustering, the authors clustered regions based on dish preference and ingredient usage separately. Findings: Experimental results show that, among 20 types of traditional Chinese cuisines, Sichuan cuisine is most favored across all regions in China. Geographical proximity is the more closely related to differences of regional dish preference than climate proximity. Originality/value: Different from traditional definitions of regions to which cuisine belong, the authors found new association between region and cuisine based on dish preference from social media and ingredient usage of dishes. Using social media may overcome problems with using traditional questionnaires, such as high costs and long cycle for questionnaire design and answering. [ABSTRACT FROM AUTHOR]

Social media, including Twitter, has become an important source for disaster response. Yet most studies focus on a very limited amount of geotagged data (approximately 1% of all tweets) while discarding a rich body of data that contains location expressions in text. Location information is crucial to understanding the impact of disasters, including where damage has occurred and where the people who need help are situated. In this paper, we propose a novel two-stage machine learning- and deep learning-based framework for power outage detection from Twitter. First, we apply a probabilistic classification model using bag-of-ngrams features to find true power outage tweets. Second, we implement a new deep learning method–bidirectional long short-term memory networks–to extract outage locations from text. Results show a promising classification accuracy (86%) in identifying true power outage tweets, and approximately 20 times more usable tweets can be located compared with simply relying on geotagged tweets. The method of identifying location names used in this paper does not require language- or domain-specific external resources such as gazetteers or handcrafted features, so it can be extended to other situational awareness analyzes and new applications. [ABSTRACT FROM AUTHOR]

This study aims to determine whether Twitter data of the firms has a significant correspondence with respect to the firms, to cluster Twitter feeds of the firms and to find out which cluster has the maximum interaction through analyzing the Twitter data of the rival firms operating in different sectors. In this context, Twitter data shared by competitors operating in the cosmetics, electronics and marketplace sectors during 2017 were analyzed by following the process of Social Media Mining. The significant correspondence of Twitter variables of the firms was determined by the Correspondence Analysis. Twitter feeds of the firms were clustered with categories "Special Offer", "Competition & Event", "Product", "Social", "Support & Feedback" and "Special Interaction" by using a number of Text Mining pre-processing methods. Since the majority of the interactions obtained by the firms came from the minority of the feeds, which cluster received more interaction was analyzed with the help of the Pareto Principle. [ABSTRACT FROM AUTHOR]

Currently users on social media post their opinion and feelings about almost everything. This online behavior has led to numerous applications where social media data are used to measure public opinion in a similar way as a poll or a survey. In this paper, we will present an application of social media mining for the art market. To the best of our knowledge, this will be the first attempt to mine social media to extract quantitative and qualitative data for the art market. Although there are previous works on analyzing and predicting other markets, these methodologies cannot be applied directly to the art market. In our proposed methodology, artists will be treated as brands. That is, we will mine Twitter posts that mention specific artists’ names and attempt to rank artists in a similar manner as brand equity and awareness would be measured. The particularities of the art market are considered mainly in the construction of a topic-specific user network where user expertise and influence is evaluated and later used to rank artists. The proposed ranking system is evaluated against two other available systems to identify the advantages it can offer. [ABSTRACT FROM AUTHOR]

Mining social media messages such as tweets, blogs, and Facebook posts for health and drug related information has received significant interest in pharmacovigilance research. Social media sites (e.g., Twitter), have been used for monitoring drug abuse, adverse reactions to drug usage, and analyzing expression of sentiments related to drugs. Most of these studies are based on aggregated results from a large population rather than specific sets of individuals. In order to conduct studies at an individual level or specific groups of people, identifying posts mentioning intake of medicine by the user is necessary. Toward this objective we develop a classifier for identifying mentions of personal intake of medicine in tweets. We train a stacked ensemble of shallow convolutional neural network (CNN) models on an annotated dataset. We use random search for tuning the hyper-parameters of the CNN models and present an ensemble of best models for the prediction task. Our system produces state-of-the-art results, with a micro-averaged F-score of 0.693. We believe that the developed classifier has direct uses in the areas of psychology, health informatics, pharmacovigilance, and affective computing for tracking moods, emotions, and sentiments of patients expressing intake of medicine in social media. [ABSTRACT FROM AUTHOR]

Sentiment analysis has become more crucial after the rise of social media, especially the Twitter since it provides structured and publicly available data. TwitterSentiDetector is a domain-dependent and unsupervised Twitter sentiment analyser that focuses on the differences occurred by the informal language used in Twitter. TwitterSentiDetector uses natural language processing techniques alongside the proposed linguistic methods to classify sentiments of tweets into positive, negative, and neutral through the polarity scores obtained from sentiment lexicons. According to tests on widely used Twitter data-sets that contain manually detected sentiments labels alongside tweets, TwitterSentiDetector’s sentiment detection ratio is calculated as up to 69%. When the target sentiment classes are decreased to positive and negative, the detection ratio is increased up to 87%. The results are calculated very similarly when the same data-set is evaluated by the proposed tweet-level context aware sentiment analysis module which confirms the validity of each approach. [ABSTRACT FROM AUTHOR]

Nowadays, social media analysis systems are feeding on user contributed data, either for beneficial purposes, such as emergency management, or for user profiling and mass surveillance. Here, we carry out a discussion about the power and pitfalls of public accessibility to social media-based systems, with specific regards to the emergency management application EARS (Earthquake Alert and Report System). We investigate whether opening such systems to the population at large would further strengthen the link between communities of volunteer citizens, intelligent systems, and decision makers, thus going in the direction of developing more sustainable and resilient societies. Our analysis highlights fundamental challenges and provides interesting insights into a number of research directions with the aim of developing human-centered social media-based systems. [ABSTRACT FROM AUTHOR]

The detection of mental disorders through social media has received significant attention. With the growing prevalence of Autism Spectrum Disorder (ASD) and the inherent difficulties in diagnosing adults, researchers have attempted to identify undiagnosed adults. Previous studies have primarily concentrated on analyzing ASD characteristics rather than directly detecting ASD. The current study aims to propose a novel framework to assist in identifying the "lost generation" of ASD adults using their social media posts. Combining traditional and deep learning methods makes it possible to model complex aspects of ASD diagnostic characteristics, which have been relatively overlooked in previous studies. To accomplish this, specific formalizations for users' patterns of interest as a main ASD diagnostic characteristic are proposed first. The latent linguistic and semantic features of ASD users' postings are then modeled using deep and transformer-based language models. Finally, all these different aspects are considered together to train a detection model by employing the multi-view learning approach. The experiments show that the feature of idiosyncratic interests has more discriminative power than limited and repetitive interests. The results also indicate that the early fusion of interest-related features along with deep linguistic features outperforms the other examined feature combinations. Additionally, the proposed ' i f − i u f ' fusion model demonstrates improved performance in capturing patterns of interests, compared to baselines. These findings suggest the potential application of the proposed framework towards indirectly identifying ASD users on social media, as evidenced by achieving precision and recall rates of 85% and 82% respectively on the used sampled dataset. [ABSTRACT FROM AUTHOR]

Due to growing concerns about electronic waste (e-waste), manufacturers are constantly pressured to adopt sustainable product development. In this regard, a product-service system (PSS) is a circular economy business model that offers a potential solution. Consumers are considered to play a vital role as key stakeholders in waste management and PSS. Although a substantial amount of research has been conducted on the consumer-facing sustainability aspects of PSS, most studies have neglected waste management in product-oriented PSS. On the other hand, research has either discussed sustainability theoretically or used traditional methods such as surveys to obtain consumers' opinions regarding the functionality of PSSs and the sustainability of their programs. Consequently, in the present study, the success of product-oriented PSSs in various aspects of waste management is evaluated utilizing a method centered around social media as the primary source of consumer feedback through machine learning (ML) algorithms and artificial intelligence (AI) methods. The novelty of this study stems from its determination of the significance and level of consumer satisfaction over an extended period to identify major topics among consumer opinions on social media. The proposed method was used in Waste Electrical and Electronic Equipment (WEEE) to evaluate leading corporations, such as Apple, Samsung, and Google. In addition, the Delphi method was implemented, and the reasons behind extracted trends are discussed based on experts' opinions. The results indicate that 'cost,' 'trust,' 'convenience,' 'awareness and advertisement,' 'benefits,' and 'data privacy and security' were the major topics identified. Moreover, 'cost' was the most important factor in determining whether consumers participate in waste management programs. Finally, this study can provide businesses with a six-year guide on how consumers have responded to their programs and concepts regarding WEEE. [ABSTRACT FROM AUTHOR]