Health-related Tweets Research Articles

“Mental Health” as Defined by Twitter: Frames, Emotions, Stigma

ABSTRACT This study analyzes the general public’s framing of ‘mental health’ and critically assesses the implications of these findings. A mismatch between how people think about mental health and what messages are used in mental health campaigns may hinder attempts to improve mental health awareness and reduce stigma. We have conducted frame analysis by using a combination of topic modeling and sentiment analysis, examining 10 years of mental health-related tweets (n = 695,414). The results reveal seven distinctive mental health frames: ‘Awareness’, ‘Feelings and Problematization’, ‘Classification’, ‘Accessibility and Funding’, ‘Stigma’, ‘Service’, and ‘Youth’ (arranged by salience). In analyzing these frames, we have learned that (1) the general awareness about mental health relates to mental illness, while health and well-being framing, although present, is prone to low quality of information, (2) mental health discourse is often used to problematize social issues and externalize personal anxieties, which tends toward trivialization and, possibly, treatment delays, (3) mental health discourse often revolves around popularized mental illness (e.g., depression, anxiety, but not neurocognitive diseases), (4) the mental health ‘Stigma’ frame is not overly pronounced; it revolves around violence, fear, and madness, (5) mental health is frequently politicized, especially concerning gun laws in the US and service accessibility and funding in the UK. Additionally, some narrower frames discovered may warrant further examination. For instance, PTSD is mostly framed around veterans and suicide, ADHD around youth, and substance abuse in relation to women, teens, and impoverished.

A topic modeling framework for spatio-temporal information management

Real-time processing and learning of conflicting data, especially messages coming from different ideas, locations, and time, in a dynamic environment such as Twitter is a challenging task that recently gained lots of attention. This paper introduces a framework for managing, processing, analyzing, detecting, and tracking topics in streaming data. We propose a model selector procedure with a hybrid indicator to tackle the challenge of online topic detection. In this framework, we built an automatic data processing pipeline with two levels of cleaning. Regular and deep cleaning are applied using multiple sources of meta knowledge to enhance data quality. Deep learning and transfer learning techniques are used to classify health-related tweets, with high accuracy and improved F1-Score. In this system, we used visualization to have a better understanding of trending topics. To demonstrate the validity of this framework, we implemented and applied it to health-related twitter data from users originating in the USA over nine months. The results of this implementation show that this framework was able to detect and track the topics at a level comparable to manual annotation. To better explain the emerging and changing topics in various locations over time the result is graphically displayed on top of the United States map.

Assessment of Beliefs and Attitudes About Statins Posted on Twitter

Statins are prescribed to help lower cholesterol levels but have poor adherence rates. Attitudes or beliefs toward medications are important to ascertain because they may be associated with patient behavior. To identify health-related discussion in Twitter posts mentioning a statin and analyze the content within these posts. This qualitative study included 11 852 posts related to 1 of 8 statins (7 licensed for use in the United States and 5 licensed in the United Kingdom) collected from May 10, 2013, to August 28, 2018. Twitter posts were identified as health related or not, and if health related, whether they were posted by a statin user, someone who knows a statin user, a health care professional, or undetermined. The health-related tweets were classified by the type of information posted, such as a belief about the medication, an adverse event, a question, or a reference to the cost of the medication. Data were analyzed from January 22 to November 19, 2019. The number of posts by each user type identified and the categories identified by analyzing the content. Of the 11 852 Twitter posts about statins, 5201 (43.9%) were health related. The most frequent posts provided resource information, such as a link to a journal article (1824 of 5201 [35.1%]). The second most frequent type of health-related posts included personal beliefs or attitudes toward statins (1097 of 5201 [21.1%]). Personal experiences of adverse events (353 of 5201 [6.8%]), discussions about dosage (320 of 5201 [6.2%]), and questions about statins (191 of 5201 [3.7%]) followed. Posts indicated polarized beliefs and attitudes to statins from saving lives to causing death. Some beliefs, such as the direct confirmation that the use of statins mitigates the effects of an unhealthy diet, have not been extensively highlighted as common practice in the literature. This qualitative content analysis of Twitter posts about statins provides insights into beliefs about statins. Patient perspectives gathered from social media may help to inform research and improve public health messages and communication between health care professionals and patients.

Trustworthy Health-Related Tweets on Social Media in Saudi Arabia: Tweet Metadata Analysis

BackgroundSocial media platforms play a vital role in the dissemination of health information. However, evidence suggests that a high proportion of Twitter posts (ie, tweets) are not necessarily accurate, and many studies suggest that tweets do not need to be accurate, or at least evidence based, to receive traction. This is a dangerous combination in the sphere of health information.ObjectiveThe first objective of this study is to examine health-related tweets originating from Saudi Arabia in terms of their accuracy. The second objective is to find factors that relate to the accuracy and dissemination of these tweets, thereby enabling the identification of ways to enhance the dissemination of accurate tweets. The initial findings from this study and methodological improvements will then be employed in a larger-scale study that will address these issues in more detail.MethodsA health lexicon was used to extract health-related tweets using the Twitter application programming interface and the results were further filtered manually. A total of 300 tweets were each labeled by two medical doctors; the doctors agreed that 109 tweets were either accurate or inaccurate. Other measures were taken from these tweets’ metadata to see if there was any relationship between the measures and either the accuracy or the dissemination of the tweets. The entire range of this metadata was analyzed using Python, version 3.6.5 (Python Software Foundation), to answer the research questions posed.ResultsA total of 34 out of 109 tweets (31.2%) in the dataset used in this study were classified as untrustworthy health information. These came mainly from users with a non-health care background and social media accounts that had no corresponding physical (ie, organization) manifestation. Unsurprisingly, we found that traditionally trusted health sources were more likely to tweet accurate health information than other users. Likewise, these provisional results suggest that tweets posted in the morning are more trustworthy than tweets posted at night, possibly corresponding to official and casual posts, respectively. Our results also suggest that the crowd was quite good at identifying trustworthy information sources, as evidenced by the number of times a tweet’s author was tagged as favorited by the community.ConclusionsThe results indicate some initially surprising factors that might correlate with the accuracy of tweets and their dissemination. For example, the time a tweet was posted correlated with its accuracy, which may reflect a difference between professional (ie, morning) and hobbyist (ie, evening) tweets. More surprisingly, tweets containing a kashida—a decorative element in Arabic writing used to justify the text within lines—were more likely to be disseminated through retweets. These findings will be further assessed using data analysis techniques on a much larger dataset in future work.

A Novel Method to Detect Public Health in Online Social Network Using Graph-based Algorithm

INTRODUCTION: Twitter has played an important role in the social life of people. The health-related tweets are extracted and find the spread of epidemic disease on network. It can provide as a starting place of individual data to learn the physical condition of users.OBJECTIVES: Key objective is to develop graph-based algorithm to detect public health in online social network.METHODS: The proposed method collect the tweets relating to general health in twitter using the min-cut algorithm. The algorithm finds the minimum cut off an undirected edge-weighted graph. The runtime of the algorithm seems to be faster than other graph algorithms. Min-cut is reliable and good in network optimization and prevents redundancy.RESULTS: To evaluate the performance, we utilize the health dataset on the detection of epidemic disease. The proposed method using a graph-based algorithm is the best in terms of accuracy, precision, and recall. With respect to the confusion matrix, Min-cut provides the highest true positive when compared to Text rank and K-Means algorithm.CONCLUSION: Proposed health detection method using graph-based algorithm is better than Text Rank and K-Means in all aspects.

Extracting health-related causality from twitter messages using natural language processing

BackgroundTwitter messages (tweets) contain various types of topics in our daily life, which include health-related topics. Analysis of health-related tweets would help us understand health conditions and concerns encountered in our daily lives. In this paper we evaluate an approach to extracting causalities from tweets using natural language processing (NLP) techniques.MethodsLexico-syntactic patterns based on dependency parser outputs are used for causality extraction. We focused on three health-related topics: “stress”, “insomnia”, and “headache.” A large dataset consisting of 24 million tweets are used.ResultsThe results show the proposed approach achieved an average precision between 74.59 to 92.27% in comparisons with human annotations.ConclusionsManual analysis on extracted causalities in tweets reveals interesting findings about expressions on health-related topic posted by Twitter users.

Hybrid System for Information Extraction from Social Media Text: Drug Abuse Case Study

Social media are becoming widely used in the healthcare field as a patients-caregivers communication tool giving birth to new sources of information rich with the knowledge that may improve this field. Therefore, social media data analysis becomes a real business requirement for healthcare industrials and data scientists.However, regarding their complexity and unstructured character, existing natural language processing tools cannot succeed their exploitation. In the literature, a wide range of approaches appeared based on dictionaries, linguistic patterns and machine learning having their strengths and weaknesses.In this work, we propose a hybrid system combining the above approaches by taking the advantage of each of them to extract structured and salient drug abuse information from health-related tweets. We improve the system accuracy by real time update of the domain dictionary. We collected 1000000 tweets and we conducted different experiments showing the advantage of hybridization on efficient information extraction from social media data.

To Retweet or Not to Retweet: Understanding What Features of Cardiovascular Tweets Influence Their Retransmission

Twitter is one of the largest social networking sites (SNSs) in the world, yet little is known about what cardiovascular health related tweets go viral and what characteristics are associated with retransmission. The current study aims to identify a function of the observable characteristics of cardiovascular tweets, including characteristics of the source, content, and style that predict the retransmission of these tweets. We identified a random sample of 1,251 tweets associated with CVD originating from the United States between 2009 and 2015. Automated coding was conducted on the affect values of the tweets as well as the presence/absence of any URL, mention of another user, question mark, exclamation mark, and hashtag. We hand-coded the tweets’ novelty, utility, theme, and source. The count of retweets was positively predicted by message utility, health organization source, and mention of user handle, but negatively predicted by the presence of URL and nonhealth organization source. Regarding theme, compared to the tweets focusing on risk factor, tweets on treatment and management predicted fewer retweets while supportive tweets predicted more retweets. These findings suggest opportunities for harnessing Twitter to better disseminate cardiovascular educational and supportive information on SNSs.

Utilizing Twitter data for analysis of chemotherapy

ObjectiveTwitter has become one of the most popular social media platforms that offers real-world insights to healthy behaviors. The purpose of this study was to assess and compare perceptions about chemotherapy of patients and health-care providers through analysis of chemo-related tweets. Materials and methodsCancer-related Twitter accounts and their tweets were obtained through using Tweepy (Python library). Multiple text classification algorithms were tested to identify the models with best performance in classifying the accounts into individual and organization. Chemotherapy-specific tweets were extracted from historical tweetset, and the content of these tweets was analyzed using topic model, sentiment analysis and word co-occurrence network. ResultsUsing the description in Twitter users’ profiles, the accounts related with cancer were collected and coded as individual or organization. We employed Long Short Term Memory (LSTM) network with GloVe word embeddings to identify the user into individuals and organizations with accuracy of 85.2%. 13, 273 and 14,051 publicly available chemotherapy-related tweets were retrieved from individuals and organizations, respectively. The content of the chemo-related tweets was analyzed by text mining approaches. The tweets from individual accounts pertained to personal chemotherapy experience and emotions. In contrast with the personal users, professional accounts had a higher proportion of neutral tweets about side effects. The information about the assessment of response to chemotherapy was deficient from organizations on Twitter. DiscussionExamining chemotherapy discussions on Twitter provide new lens into content and behavioral patterns associated with treatments for cancer patients. The methodology described herein allowed us to collect relatively large number of health-related tweets over a greater time period and exploit the potential power of social media, which provide comprehensive view on patients’ perceptions of chemotherapy. ConclusionThis study sheds light on using Twitter data as a valuable healthcare data source for helping oncologists (organizations) in understanding patients’ experiences while undergoing chemotherapy, in developing personalize therapy plans, and a supplement to the clinical electronic medical records (EMRs).

Real-time processing of social media with SENTINEL: A syndromic surveillance system incorporating deep learning for health classification

Interest in real-time syndromic surveillance based on social media data has greatly increased in recent years. The ability to detect disease outbreaks earlier than traditional methods would be highly useful for public health officials. This paper describes a software system which is built upon recent developments in machine learning and data processing to achieve this goal. The system is built from reusable modules integrated into data processing pipelines that are easily deployable and configurable. It applies deep learning to the problem of classifying health-related tweets and is able to do so with high accuracy. It has the capability to detect illness outbreaks from Twitter data and then to build up and display information about these outbreaks, including relevant news articles, to provide situational awareness. It also provides nowcasting functionality of current disease levels from previous clinical data combined with Twitter data.The preliminary results are promising, with the system being able to detect outbreaks of influenza-like illness symptoms which could then be confirmed by existing official sources. The Nowcasting module shows that using social media data can improve prediction for multiple diseases over simply using traditional data sources.

Efficient Tool for Diabetes Tracking through Layered Bigram Approach

Today social media is playing a vital role on the users of internet. In twitter, social media, public posts tweets which include status, opinion sharing, discussion etc. These tweets may be related to different domains and health care is one of the prominent domain in which health related tweets will be shared by the users. The Domain chosen for this paper is “Health” to do opinion gathering about a particular Disease for a period of two years. Though strategic analysis of tweets data, can do predictions about the future and having the opinion to take preventive measures useful for public, Health Professionals, health Behavior Researchers (Health Education). This paper main focus is to make use of social media for Health Behavior Research like tracking the information of one particular disease “Diabetes” in detail using Bigram Text Classification. In this paper tweets are considered with the search word “Diabetes” contains fields like “User’s Opinion in text message”, source of the URL, Retweet count, Location source of the tweet etc. The Tweets (text messages) are undergone Two-Level classification i.e. Bayesian Classification at the first level based on the unigram “diabetes” & Text Classification through Feature Extraction at the second level using “Term Tweet Classification Algorithm”. In this paper, some of the predictions regarding Diabetes are “People with Symptoms of Diabetes”, “People suffering from Type1 level Diabetes”, “People suffering from Type2 level Diabetes”, People Death information due to Diabetes” & “People using Insulin for Diabetes”.

On Quantifying Diffusion of Health Information on Twitter.

With the increasing use of digital technologies, online social networks are emerging as major means of communication. Recently, social networks such as Facebook and Twitter are also being used by consumers, care providers (physicians, hospitals), and government agencies to share health related information. The asymmetric user network and the short message size have made Twitter particularly popular for propagating health related content on the Web. Besides tweeting on their own, users can choose to retweet particular tweets from other users (even if they do not follow them on Twitter.) Thus, a tweet can diffuse through the Twitter network via the follower-friend connections. In this paper, we report results of a pilot study we conducted to quantitatively assess how health related tweets diffuse in the directed follower-friend Twitter graph through the retweeting activity. Our effort includes (1). development of a retweet collection and Twitter retweet graph formation framework and (2). a preliminary analysis of retweet graphs and associated diffusion metrics for health tweets. Given the ambiguous nature (due to polysemy and sarcasm) of health relatedness of tweets collected with keyword based matches, our initial study is limited to ≈ 200 health related tweets (which were manually verified to be on health topics) each with at least 25 retweets. To our knowledge, this is first attempt to study health information diffusion on Twitter through retweet graph analysis.

Tweeting as Health Communication: Health Organizations’ Use of Twitter for Health Promotion and Public Engagement

This study examined how major health organizations use Twitter for disseminating health information, building relationships, and encouraging actions to improve health. The sampled organizations were the American Heart Association, American Cancer Society, and American Diabetes Association. A content analysis was conducted on 1,583 tweets to examine these organizations’ use of Twitter's interactive features and to understand the message functions and topics of their tweets. The numbers of retweets and favorites were also measured as engagement indicators and compared by different message functions. The results revealed that all of the organizations posted original tweets most, but they differed in the degree to which they used the retweet and reply functions. Hashtags and hyperlinks were the most frequently used interactive tools. The majority of the tweets were about organization-related topics, whereas personal health–related tweets represented a relatively small portion of the sample. Followers were most likely to like and retweet personal health action-based messages.

Are Health-Related Tweets Evidence Based? Review and Analysis of Health-Related Tweets on Twitter.

BackgroundHealth care professionals are utilizing Twitter to communicate, develop disease surveillance systems, and mine health-related information. The immediate users of this health information is the general public, including patients. This necessitates the validation of health-related tweets by health care professionals to ensure they are evidence based and to avoid the use of noncredible information as a basis for critical decisions.ObjectiveThe aim of this study was to evaluate health-related tweets on Twitter for validity (evidence based) and to create awareness in the community regarding the importance of evidence-based health-related tweets.MethodsAll tweets containing health-related information in the Arabic language posted April 1-5, 2015, were mined from Twitter. The tweets were classified based on popularity, activity, interaction, and frequency to obtain 25 Twitter accounts (8 physician accounts, 10 nonofficial health institute accounts, 4 dietitian accounts, and 3 government institute accounts) and 625 tweets. These tweets were evaluated by 3 American Board–certified medical consultants and a score was generated (true/false) and interobserver agreement was calculated.ResultsA total of 625 health-related Arabic-language tweets were identified from 8 physician accounts, 10 nonofficial health institute accounts, 4 dietician accounts, and 3 government institute accounts. The reviewers labeled 320 (51.2%) tweets as false and 305 (48.8%) tweets as true. Comparative analysis of tweets by account type showed 60 of 75 (80%) tweets by government institutes, 124 of 201 (61.7%) tweets by physicians, and 42 of 101 (41.6%) tweets by dieticians were true. The interobserver agreement was moderate (range 0.78-0.22). More than half of the health-related tweets (169/248, 68.1%) from nonofficial health institutes and dietician accounts (59/101, 58.4%) were false. Tweets by the physicians were more likely to be rated “true” compared to other groups (P<.001).ConclusionsApproximately half of the medical tweets from professional accounts on Twitter were found to be false based on expert review. Furthermore, most of the evidence-based health-related tweets are posted by government institutes and physicians.

What are health-related users tweeting? A qualitative content analysis of health-related users and their messages on twitter.

BackgroundTwitter is home to many health professionals who send messages about a variety of health-related topics. Amid concerns about physicians posting inappropriate content online, more in-depth knowledge about these messages is needed to understand health professionals’ behavior on Twitter.ObjectiveOur goal was to characterize the content of Twitter messages, specifically focusing on health professionals and their tweets relating to health.MethodsWe performed an in-depth content analysis of 700 tweets. Qualitative content analysis was conducted on tweets by health users on Twitter. The primary objective was to describe the general type of content (ie, health-related versus non-health related) on Twitter authored by health professionals and further to describe health-related tweets on the basis of the type of statement made. Specific attention was given to whether a tweet was personal (as opposed to professional) or made a claim that users would expect to be supported by some level of medical evidence (ie, a “testable” claim). A secondary objective was to compare content types among different users, including patients, physicians, nurses, health care organizations, and others.ResultsHealth-related users are posting a wide range of content on Twitter. Among health-related tweets, 53.2% (184/346) contained a testable claim. Of health-related tweets by providers, 17.6% (61/346) were personal in nature; 61% (59/96) made testable statements. While organizations and businesses use Twitter to promote their services and products, patient advocates are using this tool to share their personal experiences with health.ConclusionsTwitter users in health-related fields tweet about both testable claims and personal experiences. Future work should assess the relationship between testable tweets and the actual level of evidence supporting them, including how Twitter users—especially patients—interpret the content of tweets posted by health providers.

Use of twitter among local health departments: an analysis of information sharing, engagement, and action.

BackgroundSocial media offers unprecedented opportunities for public health to engage audiences in conversations and collaboration that could potentially lead to improved health conditions. While there is some evidence that local health departments (LHDs) are using social media and Twitter in particular, little is known about how Twitter is used by LHDs and how they use it to engage followers versus disseminating one-way information.ObjectiveTo examine how LHDs use Twitter to share information, engage with followers, and promote action, as well as to discover differences in Twitter use among LHDs by size of population served.MethodsThe Twitter accounts for 210 LHDs were stratified into three groups based on size of population served (n=69 for less than 100,000; n=89 for 100,000-499,999; n=52 for 500,000 or greater). A sample of 1000 tweets was obtained for each stratum and coded as being either about the organization or about personal-health topics. Subcategories for organization included information, engagement, and action. Subcategories for personal health included information and action.ResultsOf all LHD tweets (n=3000), 56.1% (1682/3000) related to personal health compared with 39.5% (1186/3000) that were about the organization. Of the personal-health tweets, 58.5% (984/1682) involved factual information and 41.4% (697/1682) encouraged action. Of the organization-related tweets, 51.9% (615/1186) represented one-way communication about the organization and its events and services, 35.0% (416/1186) tried to engage followers in conversation, and 13.3% (158/1186) encouraged action to benefit the organization (eg, attend events). Compared with large LHDs, small LHDs were more likely to post tweets about their organization (Cramer’s V=0.06) but were less likely to acknowledge events and accomplishments of other organizations (χ2=12.83, P=.02, Cramer’s V=0.18). Small LHDs were also less likely to post personal health-related tweets (Cramer’s V=0.08) and were less likely to post tweets containing suggestions to take action to modify their lifestyle. While large LHDs were more likely to post engagement-related tweets about the organization (Cramer’s V=0.12), they were less likely to ask followers to take action that would benefit the organization (χ2=7.59, P=.02. Cramer’s V=0.08). While certain associations were statistically significant, the Cramer’s V statistic revealed weak associations.ConclusionsTwitter is being adopted by LHDs, but its primary use involves one-way communication on personal-health topics as well as organization-related information. There is also evidence that LHDs are starting to use Twitter to engage their audiences in conversations. As public health transitions to more dialogic conversation and engagement, Twitter’s potential to help form partnerships with audiences and involve them as program participants may lead to action for improved health.

What are we ‘tweeting’ about obesity? Mapping tweets with topic modeling and Geographic Information System

Public health related tweets are difficult to identify in large conversational datasets like Twitter.com. Even more challenging is the visualization and analyses of the spatial patterns encoded in tweets. This study has the following objectives: how can topic modeling be used to identify relevant public health topics such as obesity on Twitter.com? What are the common obesity related themes? What is the spatial pattern of the themes? What are the research challenges of using large conversational datasets from social networking sites? Obesity is chosen as a test theme to demonstrate the effectiveness of topic modeling using Latent Dirichlet Allocation (LDA) and spatial analysis using Geographic Information System (GIS). The dataset is constructed from tweets (originating from the United States) extracted from Twitter.com on obesity-related queries. Examples of such queries are ‘food deserts’, ‘fast food’, and ‘childhood obesity’. The tweets are also georeferenced and time stamped. Three cohesive and meaningful themes such as ‘childhood obesity and schools’, ‘obesity prevention’, and ‘obesity and food habits’ are extracted from the LDA model. The GIS analysis of the extracted themes show distinct spatial pattern between rural and urban areas, northern and southern states, and between coasts and inland states. Further, relating the themes with ancillary datasets such as US census and locations of fast food restaurants based upon the location of the tweets in a GIS environment opened new avenues for spatial analyses and mapping. Therefore the techniques used in this study provide a possible toolset for computational social scientists in general, and health researchers in specific, to better understand health problems from large conversational datasets.