How can we better use Twitter to find a person who got lost due to dementia?
ABSTRACT: Twitter is a social media platform for online message sharing. The aim of this study is to evaluate the effectiveness of using Twitter to search for people who got lost due to dementia. The online messages on Twitter, i.e., tweets, were collected through an Application Programming Interface. Contents of the tweets were analysed. The personal characteristics, features of tweets and types of Twitter users were collected to investigate their associations with whether a person can be found within a month. Logistic regression was used to identify the features that were useful in finding the missing people. Results showed that the young age of the persons with dementia who got lost, having tweets posted by police departments, and having tweets with photos can increase the chance of being found. Social media is reshaping the human communication pathway, which may lead to future needs on a new patient-care model.
Project description:<h4>Background</h4>Dementia is a prevalent disorder among adults and often subjects an individual and his or her family. Social media websites may serve as a platform to raise awareness for dementia and allow researchers to explore health-related data.<h4>Objective</h4>The objective of this study was to utilize Twitter, a social media website, to examine the content and location of tweets containing the keyword "dementia" to better understand the reasons why individuals discuss dementia. We adopted an approach that analyzed user location, user category, and tweet content subcategories to classify large publicly available datasets.<h4>Methods</h4>A total of 398 tweets were collected using the Twitter search application programming interface with the keyword "dementia," circulated between January and February 2018. Twitter users were categorized into 4 categories: general public, health care field, advocacy organization, and public broadcasting. Tweets posted by "general public" users were further subcategorized into 5 categories: mental health advocate, affected persons, stigmatization, marketing, and other. Placement into the categories was done through thematic analysis.<h4>Results</h4>A total of 398 tweets were written by 359 different screen names from 28 different countries. The largest number of Twitter users were from the United States and the United Kingdom. Within the United States, the largest number of users were from California and Texas. The majority (281/398, 70.6%) of Twitter users were categorized into the "general public" category. Content analysis of tweets from the "general public" category revealed stigmatization (113/281, 40.2%) and mental health advocacy (102/281, 36.3%) as the most common themes. Among tweets from California and Texas, California had more stigmatization tweets, while Texas had more mental health advocacy tweets.<h4>Conclusions</h4>Themes from the content of tweets highlight the mixture of the political climate and the supportive network present on Twitter. The ability to use Twitter to combat stigma and raise awareness of mental health indicates the benefits that can potentially be facilitated via the platform, but negative stigmatizing tweets may interfere with the effectiveness of this social support.
Project description:Importance:As society is increasingly becoming more networked, researchers are beginning to explore how social media can be used to study person-to-person communication about health and health care use. Twitter is an online messaging platform used by more than 300 million people who have generated several billion Tweets, yet little work has focused on the potential applications of these data for studying public attitudes and behaviors associated with cardiovascular health. Objective:To describe the volume and content of Tweets associated with cardiovascular disease as well as the characteristics of Twitter users. Design, Setting, and Participants:We used Twitter to access a random sample of approximately 10 billion English-language Tweets originating from US counties from July 23, 2009, to February 5, 2015, associated with cardiovascular disease. We characterized each Tweet relative to estimated user demographics. A random subset of 2500 Tweets was hand-coded for content and modifiers. Main Outcomes and Measures:The volume of Tweets about cardiovascular disease and the content of these Tweets. Results:Of 550?338 Tweets associated with cardiovascular disease, the terms diabetes (n?=?239?989) and myocardial infarction (n?=?269?907) were used more frequently than heart failure (n?=?9414). Users who Tweeted about cardiovascular disease were more likely to be older than the general population of Twitter users (mean age, 28.7 vs 25.4 years; P?<?.01) and less likely to be male (59?082 of 124?896 [47.3%] vs 8433 of 17?270 [48.8%]; P?<?.01). Most Tweets (2338 of 2500 [93.5%]) were associated with a health topic; common themes of Tweets included risk factors (1048 of 2500 [41.9%]), awareness (585 of 2500 [23.4%]), and management (541 of 2500 [21.6%]) of cardiovascular disease. Conclusions and Relevance:Twitter offers promise for studying public communication about cardiovascular disease.
Project description:<h4>Motivation</h4>Micro-blogging with Twitter to communicate new results, discuss ideas and share techniques is becoming central. While most Twitter users are real people, the Twitter API provides the opportunity to develop Twitter bots and to analyze global trends in tweets.<h4>Results</h4>EnrichrBot is a bot that tracks and tweets information about human genes implementing six principal functions: (i) tweeting information about under-studied genes including non-coding lncRNAs, (ii) replying to requests for information about genes, (iii) responding to GWASbot, another bot that tweets Manhattan plots from genome-wide association study analysis of the UK Biobank, (iv) tweeting randomly selected gene sets from the Enrichr database for analysis with Enrichr, (v) responding to mentions of human genes in tweets with additional information about these genes and (vi) tweeting a weekly report about the most trending genes on Twitter.<h4>Availability and implementation</h4>https://twitter.com/botenrichr; source code: https://github.com/MaayanLab/EnrichrBot.<h4>Supplementary information</h4>Supplementary data are available at Bioinformatics online.
Project description:BACKGROUND:Use of the social media website Twitter is highly prevalent and has led to a plethora of Web-based social and health-related data available for use by researchers. As such, researchers are increasingly using data from social media to retrieve and analyze mental health-related content. However, there is limited evidence regarding why people use this emerging platform to discuss mental health problems in the first place. OBJECTIVES:The aim of this study was to explore the reasons why individuals discuss mental health on the social media website Twitter. The study was the first of its kind to implement a study-specific hashtag for research; therefore, we also examined how feasible it was to circulate and analyze a study-specific hashtag for mental health research. METHODS:Text mining methods using the Twitter Streaming Application Programming Interface (API) and Twitter Search API were used to collect and organize tweets from the hashtag #WhyWeTweetMH, circulated between September 2015 and November 2015. Tweets were analyzed thematically to understand the key reasons for discussing mental health using the Twitter platform. RESULTS:Four overarching themes were derived from the 132 tweets collected: (1) sense of community; (2) raising awareness and combatting stigma; (3) safe space for expression; and (4) coping and empowerment. In addition, 11 associated subthemes were also identified. CONCLUSIONS:The themes derived from the content of the tweets highlight the perceived therapeutic benefits of Twitter through the provision of support and information and the potential for self-management strategies. The ability to use Twitter to combat stigma and raise awareness of mental health problems indicates the societal benefits that can be facilitated via the platform. The number of tweets and themes identified demonstrates the feasibility of implementing study-specific hashtags to explore research questions in the field of mental health and can be used as a basis for other health-related research.
Project description:When disaster events capture global attention users of Twitter form transient interest communities that disseminate information and other messages online. This paper examines content related to Typhoon Haiyan (locally known as Yolanda) as it hit the Philippines and triggered international humanitarian response and media attention. It reveals how Twitter conversations about disasters evolve over time, showing an issue attention cycle on a social media platform. The paper examines different functions of Twitter and the information hubs that drive and sustain conversation about the event. Content analysis shows that the majority of tweets contain information about the typhoon or its damage, and disaster relief activities. There are differences in types of content between the most retweeted messages and posts that are original tweets. Original tweets are more likely to come from ordinary users, who are more likely to tweet emotions, messages of support, and political content compared with official sources and key information hubs that include news organizations, aid organization, and celebrities. Original tweets reveal use of the site beyond information to relief coordination and response.
Project description:In recent years, the research and development of genome editing technology have been progressing rapidly, and the commercial use of genome-edited soybean started in the United States in 2019. A preceding study's results found that there is public concern with regard to the safety of high-tech foods, such as genetically modified foods and genome-edited foods. Twitter, one of the most popular social networks, allows users to post their opinions instantaneously, making it an extremely useful tool to collect what people are actually saying online in a timely manner. Therefore, it was used for collecting data on the users' concerns with and expectations of high-tech foods. This study collected and analyzed Twitter data on genome-edited foods and their labeling from May 25 to October 15 in 2019. Of 14,066 unique user IDs, 94.9% posted 5 or less tweets, whereas 64.8% tweeted only once, indicating that the majority of users who tweeted on this issue are not as intense, as they posted tweets consistently. After a process of refining, there were 28,722 tweets, of which 2,536 tweets (8.8%) were original, 326 (1.1%) were replies, and 25,860 (90%) were retweets. The numbers of tweets increased in response to government announcements and news content in the media. A total of six prominent peaks were detected during the investigation period, proving that Twitter could serve as a tool for monitoring degree of users' interests in real time. The co-occurrence network of original and reply tweets provided different words from various tweets that appeared with a certain frequency. However, the network derived from all tweets seemed to concentrate on words from specific tweets with negative overtones. As a result of sentiment analysis, 54.5% to 62.8% tweets were negative about genome-edited food and the labeling policy of the Consumer Affairs Agency, respectively, indicating a strong demand for mandatory labeling. These findings are expected to contribute to the communication strategy of genome-edited foods toward social implementation by government officers and science communicators.
Project description:BACKGROUND:At the time of this writing, the coronavirus disease (COVID-19) pandemic outbreak has already put tremendous strain on many countries' citizens, resources, and economies around the world. Social distancing measures, travel bans, self-quarantines, and business closures are changing the very fabric of societies worldwide. With people forced out of public spaces, much of the conversation about these phenomena now occurs online on social media platforms like Twitter. OBJECTIVE:In this paper, we describe a multilingual COVID-19 Twitter data set that we are making available to the research community via our COVID-19-TweetIDs GitHub repository. METHODS:We started this ongoing data collection on January 28, 2020, leveraging Twitter's streaming application programming interface (API) and Tweepy to follow certain keywords and accounts that were trending at the time data collection began. We used Twitter's search API to query for past tweets, resulting in the earliest tweets in our collection dating back to January 21, 2020. RESULTS:Since the inception of our collection, we have actively maintained and updated our GitHub repository on a weekly basis. We have published over 123 million tweets, with over 60% of the tweets in English. This paper also presents basic statistics that show that Twitter activity responds and reacts to COVID-19-related events. CONCLUSIONS:It is our hope that our contribution will enable the study of online conversation dynamics in the context of a planetary-scale epidemic outbreak of unprecedented proportions and implications. This data set could also help track COVID-19-related misinformation and unverified rumors or enable the understanding of fear and panic-and undoubtedly more.
Project description:<h4>Aim of the study</h4>Twitter has over 500 million subscribers but little is known about how it is used to communicate health information. We sought to characterize how Twitter users seek and share information related to cardiac arrest, a time-sensitive cardiovascular condition where initial treatment often relies on public knowledge and response.<h4>Methods</h4>Tweets published April-May 2011 with keywords cardiac arrest, CPR, AED, resuscitation, heart arrest, sudden death and defib were identified. Tweets were characterized by content, dissemination, and temporal trends. Tweet authors were further characterized by: self-identified background, tweet volume, and followers.<h4>Results</h4>Of 62,163 tweets (15,324, 25%) included resuscitation/cardiac arrest-specific information. These tweets referenced specific cardiac arrest events (1130, 7%), CPR performance or AED use (6896, 44%), resuscitation-related education, research, or news media (7449, 48%), or specific questions about cardiac arrest/resuscitation (270, 2%). Regarding dissemination (1980, 13%) of messages were retweeted. Resuscitation specific tweets primarily occurred on weekdays. Most users (10,282, 93%) contributed three or fewer tweets during the study time frame. Users with more than 15 resuscitation-specific tweets in the study time frame had a mean 1787 followers and most self-identified as having a healthcare affiliation.<h4>Conclusion</h4>Despite a large volume of tweets, Twitter can be filtered to identify public knowledge and information seeking and sharing about cardiac arrest. To better engage via social media, healthcare providers can distil tweets by user, content, temporal trends, and message dissemination. Further understanding of information shared by the public in this forum could suggest new approaches for improving resuscitation related education.
Project description:<b>Background:</b> Social media is ubiquitous as a tool for collaboration, networking, and dissemination. However, little is known about use of social media platforms by pulmonary and critical care medicine fellowship programs.<b>Objective:</b> We identify and characterize pulmonary and critical care fellowship programs using Twitter and Instagram, as well as the posting behaviors of their social media accounts.<b>Methods:</b> We identified all adult and pediatric pulmonary, critical care medicine (CCM), and combined pulmonary and critical care medicine (PCCM) programs in the United States using the Electronic Residency Application Service. We searched for Twitter profiles for each program between January 1, 2018, and September 30, 2018. Tweets and Twitter interactions were classified into the following three types: social, clinical, or medical education (MedEd) related. We collected data about content enhancements of tweets, including the use of pictures, graphics interchange format or videos, hashtags, links, and tagging other accounts. The types of tweets, content enhancement characteristics, and measures of engagement were analyzed for association with number of followers.<b>Results:</b> We assessed 341 programs, including 163 PCCM, 36 adult CCM, 20 adult pulmonary, 67 pediatric CCM, and 55 pediatric pulmonary programs. Thirty-three (10%) programs had Twitter accounts. Of 1,903 tweets by 33 of the 341 programs with Twitter accounts, 476 (25%) were MedEd related, 733 (39%) were clinical, and 694 (36%) were social. The median rate of tweets per month was 1.65 (interquartile range [IQR], 0.4-6.65), with 55% programs tweeting more than monthly. Accounts tweeting more often had significantly more followers than those tweeting less frequently (median, 240 followers; 25-75% IQR, 164-388 vs. median, 107 followers; 25-75% IQR, 13-188; <i>P</i> = 0.006). Higher engagement with clinical and social Twitter interactions (tweets, retweets, likes, and comments) was associated with more followers but not for the MedEd-related Twitter interactions. All types of content enhancements (pictures, graphics interchange format/videos, links, and tagging) were associated with a higher number of followers, except for hashtags.<b>Conclusion:</b> Despite the steadily increasing use of social media in medicine, only 10% of the pulmonary and critical care fellowship programs in the United States have Twitter accounts. Social and clinical content appears to gain traction online; however, additional evaluation is needed on how to effectively engage audiences with MedEd content.
Project description:The aim of this study was to investigate the Twitter experiences of adults with severe communication disabilities who use augmentative and alternative communication (AAC) to inform Twitter training and further research on the use of Twitter in populations with communication disabilities.This mixed methods research included five adults with severe communication disabilities who use AAC. It combined (a) quantitative analysis of Twitter networks and (b) manual coding of tweets with (c) narrative interviews with participants on their Twitter experiences and results.The five participants who used AAC and Twitter were diverse in their patterns and experiences of using Twitter. Twitter networks reflected interaction with a close-knit network of people rather than with the broader publics on Twitter. Conversational, Broadcast and Pass Along tweets featured most prominently, with limited use of News or Social Presence tweets. Tweets appeared mostly within each participant's micro- or meso-structural layers of Twitter.People who use AAC report positive experiences in using Twitter. Obtaining help in Twitter, and engaging in hashtag communities facilitated higher frequency of tweets and establishment of Twitter networks. Results reflected an inter-connection of participant Twitter networks that might form part of a larger as yet unexplored emergent community of people who use AAC in Twitter.