Abstract

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Background: The microblogging service Twitter is a web site that enables users to broad cast tweets or short posts. With the growing ubiquity of user generated online content via social networking web sites such as Twitter, we are experiencing a revolution in communication and information sharing. Objective: To assess whether social networking sites such as twitter can be used for oral health surveillance or not.

Methods: The content of Twitter posts meeting search criteria relating to discoloured/fractured/irregular/missing teeth/gum problems and tooth decay was investigated. A set of 394 tweets were selected from 2176 tweets over a period of one week after excluding un-interpretable and multiple tweets from the same user. Tweets were categorized as a general statement of dental problem, action taken or contemplated and the impact on the individuals.

Results: Among the 394 tweets, 67% were general statements of dental problems, 26% were regarding action taken and 7% showed the impact on daily activities. Among the actions taken 68% reported visiting a dentist, followed by 18% actively sought advice from the Twitter community.

Conclusion: Twitter data can also be used as a proxy measure of the effectiveness of public health messaging or public health campaigns. This medium can be used to disseminate oral health information and also for oral health surveillance.

Introduction

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The microblogging service Twitter is a web site that enables users to broad cast tweets or short posts. This social networking site provides an easy form of communication that allows users to broadcast and share information about their activities, opinions, and status.1 Twitter users start or join conversations about a specific topic (eg: dental pain) by exchanging instant messages, each up to 140 characters long, with others. With the growing ubiquity of user generated online content via social networking web sites such as Twitter, we are experiencing a revolution in communication and information sharing.2 Twitter data are available publically, and the data are relatively simple to access, extract, and analyse. Furthermore, tweets are reported in real time by millions of real persons from across several continents and are communicated via a variety of simple and easy-to-use formats, which are increasingly accessible in most populations3. It’s a new means for the public to communicate health concerns and also for the health care professionals a new way to communicate with patients. Twitter is currently being used successfully in public health to distribute health information to the segments of the public who access Twitter. For example, the Centres for Disease Control and Prevention (CDC) currently use Twitter as part of a larger communication and social media strategy to disseminate accurate health messages quickly and widely (CDC, 2009, 2010).2 Public health surveillance is the on-going systematic collection, analysis, and interpretation of health data from defined populations for use in planning, implementing, and evaluating public health programs (Thacker and Benkelman, 1988). The most important attributes of public health surveillance systems include simplicity, flexibility, and acceptability of the data collection instruments, as well as sensitivity, positive predictive value, representativeness, and timeliness of the data collected (Romaguera et al., 2000).4 A recent study by N. Heaivilin et al presented a novel idea and approach for dental injuries and research by using twitter data to assess dental pain experiences. Jeremy A Greene et al had evaluated the qualitative content of communication in Facebook communities and stated that Face book had been used to share the personal clinical information, to request disease specific guidance and feedback and to receive emotional support by patients with diabetes.5 It is well-known that patients use online resources to acquire health information, researchers and clinicians are just beginning to evaluate the usefulness of social networking websites such as Facebook and Twitter as a source of health information. Hence the present study was done with the aim to determine whether social networking sites like Twitter can be used for oral health surveillance or not.

Methods

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A cross-sectional study was done to evaluate tweets relating to oral diseases obtained from the social networking site Twitter. The tweets were collected for a time period of one week from 1st to 7th August 2012. The search terms included tooth decay/discoloured teeth/fractured teeth/irregular teeth/gum problems/missing teeth. After excluding un-interpretable tweets and multiple tweets from the same user 394 tweets were analysed. Prior to analysis, a coding system was developed using a different dataset obtained from the same search criteria. The content of over 100 tweets was systematically divided into three categories which included tweets related to the oral diseases, the impact of the disease on the individuals, and the actions taken over.1 This process was repeated until consensus was obtained in defining categories so that investigator could use the data consistently. This coding system was used to analyse the content of 394 tweets. Frequencies were determined for each category. Data were compiled into an excel sheet to calculate basic descriptive statistics.

Results

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A total of 394 tweets were selected from 2176 tweets over a period of one week after excluding un-interpretable and multiple tweets from the same user, using the search terms. The search terms resulted in an average of 350 tweets per day. 1782 tweets were excluded because they were not related to the dental problem. The user characteristics analysis revealed that 54% were males, 34% were females and we were unable to determine the gender of 12% of the user population. The majority of the tweets (n=262, 67%) were statements suggesting that the users were experiencing dental problems. The second most common type of content observed (n= 102, 26%) was the user stating an action he/she takes as a result of the dental problems. The most frequent actions reported were going to the dentist, seeking advice from the twitter community and taking medication. The third most common type of content related how the dental problem was having an impact on the basic activity of daily living (n=30, 7%), the most frequently reported impact was on diet, sleep and performance. Among the dental problems more number of people were having the problems related to missing teeth (n= 150, 38%) followed by fractured teeth, gum problems, tooth decay, discoloured teeth and irregular teeth. Dental health related information for those seeking advice for the problems from the twitter community is noticed to a greater extent.

Discussion

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Until recently, dental programs have focused little atten¬tion on public health surveillance,4 although surveillance data may identify research and service needs, public health surveillance is not itself, epidemiologic research6. Thus, effective surveillance requires 1) the capacity for data collection, analysis, and interpretation 2) timely dissemination of the information derived from data to people who can undertake effective prevention and control activities 3) a focus on tracking specific health outcomes, rather than only intermediate behaviours or process measures of program activity and 4) decision making for programs and policies based on cur-rent data, especially on trends over time 4. In dentistry, no records systems are widely used that are comparable to vital records or diagnosis codes taken from insurance claims and hospital discharge data. Although oral health has been monitored at the national level using oral health surveys of the ministry of health and family welfare in India, but such information lacked both a surveillance system and the capacity to conduct public oral health surveillance. Rising awareness of oral diseases and the high costs of extending traditional sensor networks mean that, we have an opportunity to harness new forms of social communication for disease surveillance. In micro-blogging services such as Twitter, users describe their experiences directly in near-real time in short 140 character tweets. Today the success of Twitter continues unabated, with over 500 million accounts and more than half of active users signing in every day. And it’s not just Twitter. The use of social media has quadrupled in the past five years: Facebook has more than 800 million active users, and Wordpress, one of the most popular blogging platforms, holds over 15 million blogs 8. Use of social media by doctors to communicate with patients raises a multitude of ethical conundrums, particularly verification of identity. But the medical potential of this untapped source of data is beginning to be recognized. Epidemiologists and computer scientists are working together to use this open data to improve disease surveillance. Despite their potential coverage, timeliness and low overhead, tweets present their own unique challenges: pre-diagnostic unedited reports mean that there is a large trust issue to resolve within the modeling technique. Despite these obvious challenges we believe there is potential for using very short messages to detect disease trends 9. Our results demonstrate that Twitter traffic can be used not only descriptively, i.e., to track users’ interest and concerns related oral health, but also to estimate the impact of dental problem on day to day activities .From a descriptive perspective, since no comparable data (e.g., survey results) are available, it is not possible to validate our results. But the trends observed are prima facie reasonable and quite consistent with expectations. In our study majority of the tweets collected were statements (67%) regarding the dental problems experienced by the individuals, followed by action taken to overcome from the impact of dental problem (26%). These results are consistent with the study done by Heaivilin et al.1 (73% & 18%) which represents that the twitter users extensively share health information relating to oral health problems. In our study majority of the tweets collected were relating to the missing teeth (150, 38%), followed by fractured teeth (106, 27%) and gum problems (73, 18%). The more number of tweets on missing teeth indicates the increased prevalence of dental caries, which is the most common reason for tooth loss in the younger age groups. Increased prevalence of fractured teeth may be related to indulgence in sports activities, accidents or violence etc. High frequency of tweets on gum problems indicates periodontal diseases as a rising problem next to dental caries due to poor maintenance of oral hygiene by the individuals. Using actual tweet contents, which often reflected the user’s own level of disease and discomfort (i.e., users were tweeting about their symptoms and pain), we included only tweets related to oral health. The accuracy of the resulting real-time estimates clearly demonstrates that the subset of tweets identified and used in our models contains information closely associated with disease activity. . Our Twitter-based model, in contrast to other approaches10, does not attempt to forecast disease activity, but instead to provide real-time estimates. Yet because our results are available ‘‘live’’ (i.e., as soon as the data are captured), our estimates are available sooner than traditional public health reports. Although, in theory, it is possible to gather diagnosis-level data in near-real time from emergency department visits , doing so at a national level would require fusing, at considerable expense, data sources from different geographic areas and multiple firms (in the case of pharmacy data or billing data): a considerable data management burden. In contrast, like search query data, Twitter data are easily and efficiently collected, and processed automatically in real time. And while search-term data related to dental problem is more available than in the past to investigators outside search engine companies, we think that our Twitter-based approach provides some unique advantages. First, the Twitter data provide more contextual information than a corpus of search queries (i.e., lists of key words), so that they can be used to investigate more than just disease activity. Second, Cooper et al.11 found that daily variations of search frequency in search query data regarding cancer were heavily influenced by new reports making search query data a necessarily ‘‘noisy’’ marker for actual disease activity. Similar data mining approaches could also be applied to search data, but require access to more context and state information (e.g., search histories rather than unlinked individual queries) than is generally made available to outside investigators by search-engine firms. This is largely because releasing fine-grained search data raises significant privacy issues, especially if it can be linked to individuals across multiple searches. In contrast, all of the Twitter data used here is placed in the public domain by the issuing user who chooses to broadcast his or her tweets to the world at large: indeed, Twitter and the Library of Congress have future plans to make every public tweet ever issued available to any interested party. Despite these promising results, there are several limitations to our study. First, the use of Twitter is neither uniform across time or geography. Mondays are usually the busiest for Twitter traffic, while the fewest tweets are issued on Sundays; also, people in few places produce far more tweets per person than other places. Second, the demographic of Twitter users do not represent the general population, and in fact, the exact demographics of the Twitter population, especially the Twitter population that would tweet about health related concerns, is unknown and not easy to estimate. Finally, we need to determine how accurately Twitter can estimate other population-based measures of disease activity.

Conclusion

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If future results are consistent with our findings, Twitter-based surveillance efforts may provide an important and cost-effective supplement to traditional disease-surveillance systems, especially in urban areas where tweet density is high. We propose that Twitter data can also be used as a proxy measure of the effectiveness of public health messaging or public health campaigns. Our ability to detect trends and confirm observations from traditional surveillance approaches make this new form of surveillance a promising area of research at the interface between computer science, epidemiology, and dental public health.

References

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Source(s) of Funding

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None

Competing Interests

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None

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