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Natural language processing (NLP) plays a pivotal role in modern life by enabling computers to comprehend, analyze, and respond to human language meaningfully, thereby offering exciting new opportunities. As social media platforms experience a surge in global usage, the imperative to capture and better understand the messages disseminated within these networks becomes increasingly crucial. Moreover, the occurrence of adverse events, such as the emergence of a pandemic or conflicts in various parts of the world, heightens social media users' inclinations towards these platforms. In this context, this paper aims to explore the scientific literature dedicated to the utilization of NLP in social media research, with the goal of highlighting trends, keywords, and collaborative networks within the authorship that contribute to the proliferation of papers in this field. To achieve this objective, we extracted and analyzed 1852 papers from the ISI Web of Science database. An initial observation reveals a remarkable annual growth rate of 62.18%, underscoring the heightened interest of the academic community in this domain. This paper includes an n-gram analysis and a review of the most cited papers in the extracted database, offering a comprehensive bibliometric analysis. The insights gained from these efforts provide essential perspectives and contribute to identifying pertinent issues in social media analysis addressed through the application of NLP. [ABSTRACT FROM AUTHOR]

With the surge of searching and reading online health‐based articles, maintaining the quality and credibility of online health‐based articles has become crucial. The circulation of deceptive health information on numerous social media sites can mislead people and can potentially cause adverse effects on people's health. To address these problems, this work uses deep learning approaches to automate the assessment and scoring of online health‐related articles' credibility. The paper proposed an Attention‐based Recurrent Multichannel Convolutional Neural Network (ARMCNN) model. The proposed model incorporates a BiLSTM layer, a multichannel CNN layer, and an attention layer and predicts the credibility of online health information. To perform a reliable evaluation of the presented model, we utilize the health articles reviewed by the experts, compiled in a labeled dataset termed "Pubhealth," which consists of thousands of health articles. The results are evaluated using five performance measures, accuracy, precision, recall, f1‐score, and area under the ROC curve (AUC). Furthermore, we extensively compared the proposed model with different deep learning and machine learning models such as Long short‐term memory (LSTM), Bidirectional LSTM, CNN (Convolutional neural network), and RNN‐CNN. The experimental results showed that the proposed model produced state‐of‐the‐art performance on the used dataset by achieving an accuracy of 0.88, precision of 0.92, recall of 0.87, f1‐score of 0.90, and AUC of 0.94. Further, the proposed model yielded better performance than other benchmarked techniques for the credibility assessment of online health articles. [ABSTRACT FROM AUTHOR]

In natural language processing, text sentiment analysis is one of the important branches. It refers to the use of text mining and other technologies to extract attitudes, opinions, and other information from texts containing emotional information for analysis. Traditional sentiment analysis methods can be roughly divided into two categories: one is dictionary-based methods, and the other is machine learning-based methods. The former relies on the quality of the sentiment dictionary, while the latter relies on a large amount of high-quality data, so both have certain limitations. In text sentiment analysis research, word-level and sentence-level sentiment information extraction is a basic research task and has important research value. Through research, it is found that domain knowledge and context are two important factors influencing the extraction of emotional information. To this end, this paper proposes a text sentiment analysis method that integrates multiple features and constructs three features, which are based on the sentiment value feature of the dictionary, the expression feature, and the improved semantic feature, which are combined to build a text sentiment classification model. Aiming at the colloquial, irregular, and diverse features of English social media texts, this paper proposes a multilevel feature representation method. The sentiment classification experiments on English text show that the multilevel features proposed in this paper can effectively improve the F1_macro and accuracy of multiple model classifications. Compared with the existing research, the model in this paper improves the effect the most obvious. [ABSTRACT FROM AUTHOR]

The democratization of access to internet and social media has given an opportunity for every individual to openly express his or her ideas and feelings. Unfortunately, this has also created room for extremist, racist, misogynist, and offensive opinions expressed either as articles, posts, or comments. While controlling offensive speech in English-, Spanish-, and French- speaking social media communities and websites has reached a mature level, it is much less the case for their counterparts in Arabic-speaking countries. This paper presents a transfer learning solution to detect hateful and offensive speech on Arabic websites and social media platforms. This paper will compare the performance of different BERT-based models trained to classify comments as either abusive or neutral. The training dataset contains comments in standard Arabic as well as four dialects. We will also use their English translations for comparative purposes. The models were evaluated based on five metrics: Accuracy, Precision, Recall, F1-Score, and Confusion Matrix. [ABSTRACT FROM AUTHOR]

With the advent of the Internet era, users from numerous countries can express their opinions on social media platforms represented by Twitter. Unearthing people's image perceptions of cities from tweets helps relevant organizations understand the image that cities present on mainstream social media and take targeted measures to shape a good international image, which can enhance international tourists' willingness to travel and strengthen city's tourism competitiveness. This paper collects nearly 130,000 tweets related to "Beijing" ("Peking") from 2017–2021 through web-crawler technology, and uses Term Frequency-Inverse Document Frequency (TF-IDF) keywords statistics, Latent Dirichlet Allocation (LDA) topic mining, and Valence Aware Dictionary and sEntiment Reasoner (VADER) sentiment analysis to further summarize the characteristics of Beijing's international image and propose strategies to communicate its international image. This research aims to tap into the international image of Beijing presented on Twitter, and provide data support for the relevant Chinese and Beijing authorities to develop communication strategies, as well as providing a reference for other cities aiming to manage their international image. [ABSTRACT FROM AUTHOR]

Intention Analysis is a computational task that analyzes people's desires, wishes, and attitudes from user-generated texts. This sub-field of text mining has recently attracted research interest. This research paper provides an overview and an analysis of the latest studies in this field. These studies were categorized and summarized according to their contributions and the techniques they used. Several proposed approaches and some real applications were investigated in depth and presented in detail. Moreover, some related fields to intention analysis such as Transfer Learning (TL), Spam Detection (SD), and Building Resources (BR) were discussed in this survey of the literature dedicated to Intention Analysis. The aim of this survey is to give a comprehensive view of the intention analysis field supported by a number of graphics and summary tables about the literature. The paper concludes by identifying a number of research topics that can be promising for future research. [ABSTRACT FROM AUTHOR]

Indonesian cuisine, especially Bakso, is one of the trendy foods that many travelers try to consume. Among the variations of Bakso, Bakso Malang is one of the most popular variants that is much talked about in social media. The present study analyzes the sentiment text mining of Bakso Malang business on post covid period from a natural language processing (NLP) social perspective. The data was taken from scraping to social media by "bakso malang" keywords. A total of 331 lexicon-filtering Tweets were gathered in the middle of March 2023. The corpus data were analyzed by valance aware dictionary and sentiment reasoner (Vader) sentiment analysis. K-means conducted the cluster of expressions. The result shows the variation of sentiments expressed in social media by anger, disgust, fear, joy, sadness, and surprise with 1.21%, 1.21%, 4.83%, 32.33%, 3.02%, and 57.40%, respectively. Many contents were discussed with keywords of "malang", "bakso", "enak", "makan", "foodfess2", "mau", and "pengen". Behavioural insight shows joy expression when discussing Bakso Malang. Further insights and recommendations are discussed in the paper. [ABSTRACT FROM AUTHOR]

Social media (e.g., Twitter and Facebook) can be regarded as vital sources of information during disasters to improve situational awareness (SA) and disaster management since they play a significant role in the rapid spread of information in the event of a disaster. Due to the volume of data is far beyond the capabilities of manual examination, existing works utilize natural language processing methods based on keywords, or classification models relying on features derived from text and other metadata (e.g., user profiles) to extract social media data contributing to SA and automatically categorize them into the relevant classes (e.g., damage and donation). However, the design of the classification schema and the associated information extraction methods are far less than straightforward and highly depend on: (1) the event type, (2) the study or analysis purpose, and (3) the social media platform used. To this end, this paper reviews the literature for extracting social media data and provides an overview of classification schemas that have been used to assess SA in events involving natural hazards from five different analytical perspectives (content, temporal, user, sentiment, and spatiotemporal) by discussing the prevalent topic categories, disaster event types, purpose of studies, and platforms utilized from each schema. Finally, this paper summarizes classification methods, and platforms that are most commonly used for each disaster event type, and outlines a research agenda with recommendations for future innovations. [ABSTRACT FROM AUTHOR]

Natural language processing (NLP) plays a pivotal role in modern life by enabling computers to comprehend, analyze, and respond to human language meaningfully, thereby offering exciting new opportunities. As social media platforms experience a surge in global usage, the imperative to capture and better understand the messages disseminated within these networks becomes increasingly crucial. Moreover, the occurrence of adverse events, such as the emergence of a pandemic or conflicts in various parts of the world, heightens social media users’ inclinations towards these platforms. In this context, this paper aims to explore the scientific literature dedicated to the utilization of NLP in social media research, with the goal of highlighting trends, keywords, and collaborative networks within the authorship that contribute to the proliferation of papers in this field. To achieve this objective, we extracted and analyzed 1852 papers from the ISI Web of Science database. An initial observation reveals a remarkable annual growth rate of 62.18%, underscoring the heightened interest of the academic community in this domain. This paper includes an n-gram analysis and a review of the most cited papers in the extracted database, offering a comprehensive bibliometric analysis. The insights gained from these efforts provide essential perspectives and contribute to identifying pertinent issues in social media analysis addressed through the application of NLP.

The issue of natural disasters, their management, and their prevention, requires good communication among stakeholders and between stakeholders and the population. The Nyiragongo volcanic crisis was a traumatic experience for those who were in Goma city in 2021, on 22 and 23 May. It was characterized by insufficient communication, demonstrating the lack of cohesion between stakeholders. In order to prepare a possible implementation of dedicated social media and groupware for effective communication during the Nyiragongo Volcanic Crisis, this paper presents a survey conducted among Goma citizens and proposes a possible architecture of a dedicated social media integrating text mining, which would help develop a flexible evacuation plan. Based on the results obtained, it appears that the establishment of a single information system related to the volcanic crisis can reduce the challenges related to social media where the volcano observatory, political authorities, different partners and citizens can share alert messages, locations of danger, and more comments. This unified information system would help to gather, discuss and disseminate accurate information on volcanic activities and associated hazards and risks. [ABSTRACT FROM AUTHOR]

The study of Online Social Networks offers growing opportunities to examine a number of aspects of the real world and to better understand how human society works at scale. One crucial research direction, constantly evolving over time, concerns the language adopted by users and its impact on online communities. To provide a contribution in this setting, in this paper, we adopt a multi-relational model for social network and identify a new typology of relation, namely co-interest, as an explicit common declaration of engagement towards a given topic among pair of users. Thematic communities can be, hence, derived leveraging such relations. After that, exploiting Natural Language Processing and Machine Learning techniques, we identify and define suitable analysis metrics focusing on the characteristics of the language adopted in such communities. With the objective of analyzing how the adoption of different language may impact the formation of cohesive and strong communities, to make our study general, we consider two very popular and intrinsically different social platforms, namely Twitter and Reddit. The analysis carried out in this paper aims at comparing the two social media to find interesting and novel results about how their users compose textual comments and the corresponding impact on users' interaction. Interestingly, the obtained results show that, although the main factor is related to the popularity of the user generating the content, the characteristics of the adopted language play a non negligible role in the formation of strong communities in social systems. • Investigation about language characteristics of Online Social Networks posts (OSN). • Language analysis to identify the evolution dynamics of online communities. • Correlation between potential post engagement and user adopted language style. • Relationship between the content produced in OSNs and the formation of communities. [ABSTRACT FROM AUTHOR]

With the popularization of smart mobile terminals and social media, a large amount of data containing textual information about the city has been generated on social media platforms, covering all areas of the city. This provides a new way for the study of comprehensive perception of city image. In the Internet era, users express their opinions about cities through social media platforms (e.g., Sina Weibo), and mining this information helps to understand the image of cities on mainstream social media and to target positive images to improve the competitiveness of the city's image. In this paper, 370,000 microblog messages related to “Guangzhou City” between 2019 and 2023 are collected using web crawler technology, and three typical text analysis methods are adopted: Term Frequency-Inverse Document Frequency (TF-IDF), Latent Dirichlet Allocation (LDA), and Sentiment Analysis (SnowNLP), to understand the characteristics of Guangzhou city image. gain an in-depth understanding of Guangzhou's urban image characteristics. The study shows that extensive data analysis methods based on text mining can perceive the dynamics and trends of the city in a timely manner, refine the characteristics of Guangzhou's urban image, and propose communication strategies for Guangzhou's image. This study aims to mine Guangzhou's urban image presented on Weibo, provide data support for relevant departments in China and Guangzhou to formulate communication strategies, and provide references for other cities to manage their urban image.

Background: Data collection and extraction from noisy text sources such as social media typically rely on keyword-based searching/listening. However, health-related terms are often misspelled in such noisy text sources due to their complex morphology, resulting in the exclusion of relevant data for studies. In this paper, we present a customizable data-centric system that automatically generates common misspellings for complex health-related terms, which can improve the data collection process from noisy text sources.Materials and Methods: The spelling variant generator relies on a dense vector model learned from large, unlabeled text, which is used to find semantically close terms to the original/seed keyword, followed by the filtering of terms that are lexically dissimilar beyond a given threshold. The process is executed recursively, converging when no new terms similar (lexically and semantically) to the seed keyword are found. The weighting of intra-word character sequence similarities allows further problem-specific customization of the system.Results: On a dataset prepared for this study, our system outperforms the current state-of-the-art medication name variant generator with best F1-score of 0.69 and F14-score of 0.78. Extrinsic evaluation of the system on a set of cancer-related terms demonstrated an increase of over 67% in retrieval rate from Twitter posts when the generated variants are included.Discussion: Our proposed spelling variant generator has several advantages over past spelling variant generators-(i) it is capable of filtering out lexically similar but semantically dissimilar terms, (ii) the number of variants generated is low, as many low-frequency and ambiguous misspellings are filtered out, and (iii) the system is fully automatic, customizable and easily executable. While the base system is fully unsupervised, we show how supervision may be employed to adjust weights for task-specific customizations.Conclusion: The performance and relative simplicity of our proposed approach make it a much-needed spelling variant generation resource for health-related text mining from noisy sources. The source code for the system has been made publicly available for research. [ABSTRACT FROM AUTHOR]

Traffic accident detection is an important strategy governments can use to implement policies intended to reduce accidents. They usually use techniques such as image processing, RFID devices, among others. Social network mining has emerged as a low-cost alternative. However, social networks come with several challenges such as informal language and misspellings. This paper proposes a method to extract traffic accident data from Twitter in Spanish. The method consists of four phases. The first phase establishes the data collection mechanisms. The second consists of vectorially representing the messages and classifying them as accidents or non-accidents. The third phase uses named entity recognition techniques to detect the location. In the fourth phase, locations pass through a geocoder that returns their geographic coordinates. This method was applied to Bogota city and the data on Twitter were compared with the official traffic information source; comparisons showed some influence of Twitter on the commercial and industrial area of the city. The results reveal how effective the information on accidents reported on Twitter can be. It should therefore be considered as a source of information that may complement existing detection methods. [ABSTRACT FROM AUTHOR]

COVID-19 has become a global pandemic that has affected not only the health sector but also economic, social, and psychological well-being. Individuals are using social media platforms to communicate their feelings and sentiments about the pandemic. One of the most debated topics in that regard is the vaccine. People are divided mainly into two groups, pro-vaccine and anti-vaccine. This article aims to explore Arabic Sentiment Analysis for Vaccine-Related COVID-19 Tweets (ASAVACT) to quantify sentiment polarity shared publicly, and it is considered the first and the largest human-annotated dataset in Arabic. The analysis is done using state-of-theart deep learning models that proved superiority in the field of language processing and analysis. The models are the stacked gated recurrent unit (SGRU), the stacked bidirectional gated recurrent unit (SBi-GRU), and the ensemble architecture of SGRU, SBi-GRU, and AraBERT. Additionally, this article presents the largest Arabic Twitter corpus on COVID-19 vaccination, with 32,476 annotated Tweets. The results show that the ensemble model outperformed other singular models with at least 7% accuracy enhancement. [ABSTRACT FROM AUTHOR]

Social media, fake news, and different propaganda strategies have all contributed to an increase in misinformation online during the past ten years. As a result of the scarcity of high-quality data, the present datasets cannot be used to train a deep-learning model, making it impossible to establish an identification. We used a natural language processing approach to the issue in order to create a system that uses deep learning to automatically identify propaganda in news items. To assist the scholarly community in identifying propaganda in text news, this study suggested the propaganda texts (ProText) library. Truthfulness labels are assigned to ProText repositories after being manually and automatically verified with fact-checking methods. Additionally, this study proposed using a fine-tuned Robustly Optimized BERT Pre-training Approach (RoBERTa) and word embedding using multi-label multi-class text classification. Through experimentation and comparative research analysis, we address critical issues and collaborate to discover answers. We achieved an evaluation performance accuracy of 90%, 75%, 68%, and 65% on ProText, PTC, TSHP-17, and Qprop, respectively. The big-data method, particularly with deep-learning models, can assist us in filling out unsatisfactory big data in a novel text classification strategy. We urge collaboration to inspire researchers to acquire, exchange datasets, and develop a standard aimed at organizing, labeling, and fact-checking. [ABSTRACT FROM AUTHOR]

Transportation infrastructure is vital to the well-functioning of economic activities in a region. Due to the digitalization of data storage, ease of access to large databases, and advancement of social media, large volumes of text data that relate to different aspects of transportation infrastructure are generated. Text mining techniques can explore any large amount of textual data within a limited time and with limited resource allocation for generating easy-to-understand knowledge. This study aims to provide a comprehensive review of the various applications of text mining techniques in transportation infrastructure research. The scope of this research ranges across all forms of transportation infrastructure-related problems or issues that were investigated by different text mining techniques. These transportation infrastructure-related problems or issues may involve issues such as crashes or accidents investigation, driving behavior analysis, and construction activities. A Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA)-based structured methodology was used to identify relevant studies that implemented different text mining techniques across different transportation infrastructure-related problems or issues. A total of 59 studies from both the U.S. and other parts of the world (e.g., China, and Bangladesh) were ultimately selected for review after a rigorous quality check. The results show that apart from simple text mining techniques for data pre-processing, the majority of the studies used topic modeling techniques for a detailed evaluation of the text data. Other techniques such as classification algorithms were also later used to predict and/or project future scenarios/states based on the identified topics. The findings from this study will hopefully provide researchers and practitioners with a better understanding of the potential of text mining techniques under different circumstances to solve different types of transportation infrastructure-related problems. They will also provide a blueprint to better understand the ever-evolving area of transportation engineering and infrastructure-focused studies. [ABSTRACT FROM AUTHOR]

Identifying internet spam has been a challenging problem for decades. Several solutions have succeeded to detect spam comments in social media or fraudulent emails. However, an adequate strategy for filtering messages is difficult to achieve, as these messages resemble real communications. From the Natural Language Processing (NLP) perspective, Deep Learning models are a good alternative for classifying text after being preprocessed. In particular, Long Short-Term Memory (LSTM) networks are one of the models that perform well for the binary and multi-label text classification problems. In this paper, an approach merging two different data sources, one intended for Spam in social media posts and the other for Fraud classification in emails, is presented. We designed a multi-label LSTM model and trained it on the joint datasets including text with common bigrams, extracted from each independent dataset. The experiment results show that our proposed model is capable of identifying malicious text regardless of the source. The LSTM model trained with the merged dataset outperforms the models trained independently on each dataset. [ABSTRACT FROM AUTHOR]

University towns face many challenges in the 21st century due to urbanization, increased student population, and higher educational institutions' inability to house all their students on-campus. For university towns to be resilient and sustainable, the challenges facing them must be assessed and addressed. To carry out community resilience assessments, this study adopted a novel methodological framework to harness the power of artificial intelligence and social media big data (user-generated content on Twitter) to carry out remote studies in six university towns on six continents using Text Mining, Machine Learning, and Natural Language Processing. Cultural, social, physical, economic, and institutional and governance community challenges were identified and analyzed from the historical big data and validated using an online expert survey. This study gives a global overview of the challenges university towns experience due to studentification and shows that artificial intelligence can provide an easy, cheap, and more accurate way of conducting community resilience assessments in urban communities. The study also contributes to knowledge of research in the new normal by proving that longitudinal studies can be completed remotely. [ABSTRACT FROM AUTHOR]

Twitter has become a major social media platform and has attracted considerable interest among researchers in sentiment analysis. Research into Twitter Sentiment Analysis (TSA) is an active subfield of text mining. TSA refers to the use of computers to process the subjective nature of Twitter data, including its opinions and sentiments. In this research, a thorough review of the most recent developments in this area, and a wide range of newly proposed algorithms and applications are explored. Each publication is arranged into a category based on its significance to a particular type of TSA method. The purpose of this survey is to provide a concise, nearly comprehensive overview of TSA techniques and related fields. The primary contributions of the survey are the detailed classifications of numerous recent articles and the depiction of the current direction of research in the field of TSA. [ABSTRACT FROM AUTHOR]

News dissemination in social media causes fluctuations in financial markets. (Scope) Recent advanced methods in deep learning-based natural language processing have shown promising results in financial market analysis. However, understanding how to leverage large amounts of textual data alongside financial market information is important for the investors' behavior analysis. In this study, we review over 150 publications in the field of behavioral finance that jointly investigated natural language processing (NLP) approaches and a market data analysis for financial decision support. This work differs from other reviews by focusing on applied publications in computer science and artificial intelligence that contributed to a heterogeneous information fusion for the investors' behavior analysis. (Goal) We study various text representation methods, sentiment analysis, and information retrieval methods from heterogeneous data sources. (Findings) We present current and future research directions in text mining and deep learning for correlation analysis, forecasting, and recommendation systems in financial markets, such as stocks, cryptocurrencies, and Forex (Foreign Exchange Market). [ABSTRACT FROM AUTHOR]

This study aims to conduct a survey of visitor reviews of the Plitvice Lakes National Park in Croatia to detect strengths and weaknesses of the park. In total, 15,673 reviews written in the period between 2007 and 2021 were scraped from the social media platform TripAdvisor. The research applies a comprehensive combination of multidimensional scaling, sentiment analysis, and natural language processing approaches to a sample area of international naturalistic interest. Analyzing the opinions of visitors, the authors identify: the main topics of interest related to the management of the park; and the strengths and weaknesses on the basis of definitely positive and decidedly negative reviews, respectively. The tested methodology is easily applicable for the analysis of different naturalistic contexts and protected areas, even in different countries, thanks to the use of translated reviews. The results obtained show that visitors to protected natural areas are not only interested in naturalistic and landscape aspects but also in issues such as accessibility and management of routes and visits. [ABSTRACT FROM AUTHOR]

Financial technology-based firms (FinTech) are crucial to promoting new technologies and advances in innovations related to the financial field, sustainable development, and financial inclusion. This paper aims to assess the Sustainable Development Goals (SDGs) and open innovation integrated within the public discourse in the social media of FinTech firms, using machine-learning-based social media analysis (SMA). Accordingly, we tracked the behavior of 21 firms based on the empirical material of 32,716 posts on Twitter. The outcomes showed dissimilar discourses based on FinTech firms' activities. However, it was found that only financial infrastructure, lending, and personal finance fields have a discourse related to innovation and, to a lesser extent, related to the SDGs thematic. Thus, awareness of sustainable objectives is still far from being a relevant issue for FinTech, which, in general, has neglected to significantly mention SDGs; nonetheless, innovation and related terms are a consistent topic in this area. Furthermore, hints of the implementation of the open innovation paradigm and interest in novel technologies are demonstrated, in addition to the promotion of different actors and events on social media that serve as a showcase for firms that have a presence on Twitter. [ABSTRACT FROM AUTHOR]

Objective: To mine Twitter and quantitatively analyze COVID-19 symptoms self-reported by users, compare symptom distributions across studies, and create a symptom lexicon for future research.Materials and Methods: We retrieved tweets using COVID-19-related keywords, and performed semiautomatic filtering to curate self-reports of positive-tested users. We extracted COVID-19-related symptoms mentioned by the users, mapped them to standard concept IDs in the Unified Medical Language System, and compared the distributions to those reported in early studies from clinical settings.Results: We identified 203 positive-tested users who reported 1002 symptoms using 668 unique expressions. The most frequently-reported symptoms were fever/pyrexia (66.1%), cough (57.9%), body ache/pain (42.7%), fatigue (42.1%), headache (37.4%), and dyspnea (36.3%) amongst users who reported at least 1 symptom. Mild symptoms, such as anosmia (28.7%) and ageusia (28.1%), were frequently reported on Twitter, but not in clinical studies.Conclusion: The spectrum of COVID-19 symptoms identified from Twitter may complement those identified in clinical settings. [ABSTRACT FROM AUTHOR]

Objective: Prescription medication (PM) misuse and abuse is a major health problem globally, and a number of recent studies have focused on exploring social media as a resource for monitoring nonmedical PM use. Our objectives are to present a methodological review of social media-based PM abuse or misuse monitoring studies, and to propose a potential generalizable, data-centric processing pipeline for the curation of data from this resource.Materials and Methods: We identified studies involving social media, PMs, and misuse or abuse (inclusion criteria) from Medline, Embase, Scopus, Web of Science, and Google Scholar. We categorized studies based on multiple characteristics including but not limited to data size; social media source(s); medications studied; and primary objectives, methods, and findings.Results: A total of 39 studies met our inclusion criteria, with 31 (∼79.5%) published since 2015. Twitter has been the most popular resource, with Reddit and Instagram gaining popularity recently. Early studies focused mostly on manual, qualitative analyses, with a growing trend toward the use of data-centric methods involving natural language processing and machine learning.Discussion: There is a paucity of standardized, data-centric frameworks for curating social media data for task-specific analyses and near real-time surveillance of nonmedical PM use. Many existing studies do not quantify human agreements for manual annotation tasks or take into account the presence of noise in data.Conclusion: The development of reproducible and standardized data-centric frameworks that build on the current state-of-the-art methods in data and text mining may enable effective utilization of social media data for understanding and monitoring nonmedical PM use. [ABSTRACT FROM AUTHOR]

Background: Patient narrative data in online health care forums (communities) are receiving increasing attention from the scientific community for implementing patient-centered care. Natural language processing (NLP) methods are gaining more and more attention because of the enormous data volume. However, state-of-the-art NLP still cannot meet the need of high-resolution analysis of patients' narratives. Manual qualitative analysis still plays a pivotal role in answering complicated research questions from analyzing patient narratives. Objective: This study aimed to develop a systematic framework for qualitative analysis of patient-generated narratives in online health care forums. Methods: Our systematic framework consists of 4 phases: (1) data collection, (2) data preparation, (3) content analysis, and (4) interpretation of the results. Data collection and data preparation phases are constructed based on text mining methods for identifying appropriate online health forums for data collection, differentiating posts of patients from other stakeholders, protecting patients' privacy, sampling, and choosing the unit of analysis. Content analysis phase is built on the framework method, which facilitates and accelerates the identification of patterns and themes by an interdisciplinary research team. In the end, the focus of interpretation of the results phase is to measure the data quality and interpret the findings regarding the dimensions and aspects of patients' experiences and concerns in their original contexts. Results: We demonstrated the usability of the proposed systematic framework using 2 case studies: one on determining factors affecting patients' attitudes toward antidepressants and another on identifying the disease management strategies in patient with diabetes facing financial difficulties. The framework provides a clear step-by-step process for systematic content analysis of patient narratives and produces high-quality structured results that can be used for describing patterns or regularities in patients' experiences, generating and testing hypotheses, and identifying areas of improvement in the health care systems. Conclusions: The systematic framework is a rigorous and standardized method for qualitative analysis of patient narratives. Findings obtained through such a process indicate authentic dimensions and aspects of patient experiences and shed light on patients' concerns, needs, preferences, and values, which are the core of patient-centered care. International Registered Report Identifier (IRRID): RR1-10.2196/13914 We help JMIR researchers to raise funds to pursue their research and development aimed at tackling important health and technology challenges. If you would like to show your support for this author, please donate using the button below. The funds raised will directly benefit the corresponding author of this article (minus 8% admin fees). Your donations will help this author to continue publishing open access papers in JMIR journals. Donations of over $100 may also be acknowledged in future publications. Suggested contribution levels: $20/$50/$100 [ABSTRACT FROM AUTHOR]

Background: The amount of medical and clinical-related information on the Web is increasing. Among the different types of information available, social media-based data obtained directly from people are particularly valuable and are attracting significant attention. To encourage medical natural language processing (NLP) research exploiting social media data, the 13th NII Testbeds and Community for Information access Research (NTCIR-13) Medical natural language processing for Web document (MedWeb) provides pseudo-Twitter messages in a cross-language and multi-label corpus, covering 3 languages (Japanese, English, and Chinese) and annotated with 8 symptom labels (such as cold, fever, and flu). Then, participants classify each tweet into 1 of the 2 categories: those containing a patient's symptom and those that do not.Objective: This study aimed to present the results of groups participating in a Japanese subtask, English subtask, and Chinese subtask along with discussions, to clarify the issues that need to be resolved in the field of medical NLP.Methods: In summary, 8 groups (19 systems) participated in the Japanese subtask, 4 groups (12 systems) participated in the English subtask, and 2 groups (6 systems) participated in the Chinese subtask. In total, 2 baseline systems were constructed for each subtask. The performance of the participant and baseline systems was assessed using the exact match accuracy, F-measure based on precision and recall, and Hamming loss.Results: The best system achieved exactly 0.880 match accuracy, 0.920 F-measure, and 0.019 Hamming loss. The averages of match accuracy, F-measure, and Hamming loss for the Japanese subtask were 0.720, 0.820, and 0.051; those for the English subtask were 0.770, 0.850, and 0.037; and those for the Chinese subtask were 0.810, 0.880, and 0.032, respectively.Conclusions: This paper presented and discussed the performance of systems participating in the NTCIR-13 MedWeb task. As the MedWeb task settings can be formalized as the factualization of text, the achievement of this task could be directly applied to practical clinical applications. [ABSTRACT FROM AUTHOR]

Featured Application: The health insurance choice literature has found that financial considerations, such as premiums, deductible, and maximum out-of-pocket spending caps, are important to consumers. But these financial factors are just part of the cost-benefit trade-off consumers make. Publicly available datasets often do not include these other factors. Researchers in other fields have increasingly used web data from social media platforms, such as Twitter and search engines to analyze consumer behavior using Natural Language Processing. NLP combines machine learning, computational linguistics, and computer science, to understand natural language including consumer's sentiments, attitudes, and emotions from social media. This study is among the first to use natural language from an online platform to analyze sentiments when consumers are discussing health insurance. By clarifying what the expressed attitudes or sentiments are, we get an idea of what variables we may want to include in future studies of health insurance choice. This study aims to identify sentiments that consumers have about health insurance by analyzing what they discuss on Twitter. The objective was to use sentiment analysis to identify attitudes consumers express towards health insurance and health care providers. We used an Application Programming Interface to gather tweets from Twitter with the words "health insurance" or "health plan" during health insurance enrollment season in the United States in 2016‒2017. Word association was used to find words associated with "premium," "access," "network," and "switch." Sentiment analysis established which specific emotions were associated with insurance and medical providers, using the NRC Emotion Lexicon, identifying emotions. We identified that provider networks, prescription drug benefits, political preferences, and norms of other consumers matter. Consumers trust medical providers but they fear unexpected health events. The results suggest that there is a need for different algorithms to help consumers find the plans they want and need. Consumers buying health insurance in the Affordable Care Act marketplaces in the United States choose lower-cost plans with limited benefits, but at the same time express fear about unexpected health events and unanticipated costs. If we better understand the origin of the sentiments that drive consumers, we may be able to help them better navigate insurance plan options and insurers can better respond to their needs. [ABSTRACT FROM AUTHOR]

Objective: We executed the Social Media Mining for Health (SMM4H) 2017 shared tasks to enable the community-driven development and large-scale evaluation of automatic text processing methods for the classification and normalization of health-related text from social media. An additional objective was to publicly release manually annotated data.Materials and Methods: We organized 3 independent subtasks: automatic classification of self-reports of 1) adverse drug reactions (ADRs) and 2) medication consumption, from medication-mentioning tweets, and 3) normalization of ADR expressions. Training data consisted of 15 717 annotated tweets for (1), 10 260 for (2), and 6650 ADR phrases and identifiers for (3); and exhibited typical properties of social-media-based health-related texts. Systems were evaluated using 9961, 7513, and 2500 instances for the 3 subtasks, respectively. We evaluated performances of classes of methods and ensembles of system combinations following the shared tasks.Results: Among 55 system runs, the best system scores for the 3 subtasks were 0.435 (ADR class F1-score) for subtask-1, 0.693 (micro-averaged F1-score over two classes) for subtask-2, and 88.5% (accuracy) for subtask-3. Ensembles of system combinations obtained best scores of 0.476, 0.702, and 88.7%, outperforming individual systems.Discussion: Among individual systems, support vector machines and convolutional neural networks showed high performance. Performance gains achieved by ensembles of system combinations suggest that such strategies may be suitable for operational systems relying on difficult text classification tasks (eg, subtask-1).Conclusions: Data imbalance and lack of context remain challenges for natural language processing of social media text. Annotated data from the shared task have been made available as reference standards for future studies (http://dx.doi.org/10.17632/rxwfb3tysd.1). [ABSTRACT FROM AUTHOR]