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Obesity can lead to the death of at least 2.8 million people each year1, yet the rate of obesity around the world has continuously increased over the past 30 years1. Societal changes, including increased food consumption and decreased physical activity, have been determined as two of the main drivers behind the current obesity pandemic2. Examining socio-cultural factors (i.e., attitudes or perceptions of cultural groups)3 associated with food consumption and weight loss can provide important insights to guide effective interventions and a novel surveillance approach to characterize population obesity trends from sociological perspectives. The primary goal of this study is to examine socio-cultural factors associated with food consumption and weight loss by conducting sentiment analysis on related online chatters. The secondary goal is to discuss the potential implications of being exposed to these different chatters in the online environment. Scientific evidence in support of using social media to understand socio-cultural factors and its potential implications can be illustrated in two concise assertions. First, online chatters, including discussions on social media, have been shown to be an effective data source for understanding public interests4,5. Second, prolonged participation in social media has been suggested to have impacts on users6-8.

Objective: We aim to better understand socio-cultural factors (i.e., attitudes or perceptions of cultural groups) associated with food consumption and weight loss via sentiment analysis on tweets, short messages from Twitter.

Submitted by elamb on

Social media as Twitter are used today by people to disseminate health information but also to share or exchange on their health. Based on this observation, recent studies showed that Twitter data can be used to monitor trends of infectious diseases such as influenza. These studies were mainly carried out in United States where Twitter is very popular1-4. In our knowledge, no research has been implemented in France to know whether Twitter data can be a complementary data source to monitor seasonal influenza epidemic.

Objective: To investigate whether Twitter data can be used as a proxy for the surveillance of the seasonal influenza epidemic in France and at the regional level.

Submitted by elamb on

The spread of infectious diseases is facilitated by human travel. Infectious diseases are often introduced into a population by travelers and then spread among susceptible individuals. Likewise uninfected susceptible travelers can move into populations sustaining the spread of an infectious disease.

Several disease-modeling efforts have incorporated travel data (e.g., air, train, or subway traffic) as well as census data, all in an effort to better understand the spread of infectious diseases. Unfortunately, most travel data is not fine grained enough to capture individual movements over long periods and large spaces. It does not, for example, document what happens when people get off a train or airplane. Thus, other methods have been suggested to measure how people move, including both the tracking of currency and movement of individuals using cell phone data. Although these data are finer grained, they have their own limitations (e.g., sparseness) and are not generally available for research purposes.

FourSquare is a social media application that permits users to "check-in" (i.e., record their current location at stores, restaurants, etc.) via their mobile telephones in exchange for incentives (e.g., location-specific coupons). FourSquare and similar applications (Gowalla, Yelp, etc.) generally broadcast each check-in via Twitter or Facebook; in addition, some GPS-enabled mobile Twitter clients add explicit geocodes to individual tweets.

Here, we propose the use of geocoded social media data as a real-time fine-grained proxy for human travel.

 

Objective

To use sequential, geocoded social media data as a proxy for human movement to support both disease surveillance and disease modeling efforts.

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Submitted by elamb on

Influenza is a recurrent viral disease with potential to result in pandemics. Therefore it is necessary to have a timely, responsive and accurate detection. The use of Twitter as a source for data mining in biosurveillance has been previously shown useful, and it also has a potential for real-time visualization. However these efforts target messages in English, omitting from surveillance the part of users that speaks other languages, such as Spanish.

 

Objective

Identify the potential of Twitter as a source for monitoring and visualizing content regarding Influenza-like-Illness in Spanish-speaking populations for biosurveillance purposes.

Referenced File
Submitted by elamb on

Dengue fever is a major cause of morbidity and mortality in the Republic of the Philippines (RP) and across the world. Early identification of geographic outbreaks can help target intervention campaigns and mitigate the severity of outbreaks. Electronic disease surveillance can improve early identification but, in most dengue endemic areas data pre-existing digital data are not available for such systems. Data must be collected and digitized specifically for electronic disease surveillance. Twitter, however, is heavily used in these areas; for example, the RP is among the top 20 producers of tweets in the world. If social media could be used as a surrogate data source for electronic disease surveillance, it would provide an inexpensive pre-digitized data source for resource-limited countries. This study investigates whether Twitter extracts can be used effectively as a surrogate data source to monitor changes in the temporal trend of dengue fever in Cebu City and the National Capitol Region surrounding Manila (NCR) in the RP.

Objective:

To determine whether Twitter data contains information on dengue-like illness and whether the temporal trend of such data correlates with the incidence dengue or dengue-like illness as identified by city and national health authorities.

 

Submitted by Magou on

The use of social media as a syndromic sentinel for diseases is an emerging field of growing relevance as the public begins to share more online, particularly in the area of influenza. Several applications have been developed to predict or monitor influenza activity using publicly posted or self-reported online data; however, few have prioritized accuracy at the local level. In 2016, the Cook County Department of Public Health (CCDPH) collected localized Twitter information to evaluate its utility as a potential influenza sentinel data source. Tweets from MMWR week 40 through MMWR week 20 indicating influenza-like illness (ILI) in our jurisdiction were collected and analyzed for correlation with traditional surveillance indicators. Social media has the potential to join other sentinels, such as emergency room and outpatient provider data, to create a more accurate and complete picture of influenza in Cook County.

Objective:

To determine if social media data can be used as a surveillance tool for influenza at the local level.

Submitted by elamb on

Recently, a growing number of studies have made use of Twitter to track the spread of infectious disease. These investigations show that there are reliable spikes in traffic related to keywords associated with the spread of infectious diseases like Influenza [1], as well as other Syndromes [2]. However, little research has been done using Social Media to monitor chronic conditions like Asthma, which do not spread from sufferer to sufferer. We therefore test the feasibility of using Twitter for Asthma surveillance, using techniques from NLP and machine learning to achieve a deeper understanding of what users Tweet about Asthma, rather than relying only on keyword search.

Objective

We present a Content Analysis project using Natural Language Processing to aid in Twitter-based syndromic surveillance of Asthma

Submitted by rmathes on

Vast amounts of free, real-time, localizable Twitter data offer new possibilities for public health workers to identify trends and attitudes that more traditional surveillance methods may not capture, particu- larly in emerging areas of public health concern where reliable sta- tistical evidence is not readily accessible. Existing applications include tracking public informedness during disease outbreaks. Twitter-based surveillance is particularly suited to new challenges in tobacco control. Hookah and e-cigarettes have surged in popular- ity, yet regulation and public information remain sparse, despite con- troversial health effects. Ubiquitous online marketing of these products and their popularity among new and younger users make Twitter a key resource for tobacco surveillance. 

Objective

We present results of a content analysis of tobacco-related Twitter posts (tweets), focusing on tweets referencing e-cigarettes and hookah. 

Submitted by jababrad@indiana.edu on

Social media is of considerable interest as a sensor into the thoughts, interests and health of a population. We consider three types of health events that an analyst may wish to be made aware of:

- Given a known disease, such as MERS, SARS, Measles, etc., an event corresponds to individuals contracting the disease.

- Given a set of symptoms (fever, stomach pain, etc.), an event is an unusual number of individuals1 complaining of the symptoms.

- Most generally: an event is an unusually large group of individuals who can be identified as being effected by some personal illness.

Note that to detect an “unusual number” of something, we need to count the indicators of the event, and we need to compare the current count with past counts. Further, we are generally interested in geographically constrained events, and so for this work we will focus on county-based counts. We will count the number of items (tweets or individuals) expressing the event indicator (a disease name, symptom, or classified as “personal health related” as indicated by our classifier). Our approach to detecting health related events is: filter -> classify -> detect. We first filter out tweets that contain no “health related” terms, then apply a classifier to each tweet. This classifier is designed to flag a tweet as being about “personal health” or not. We then aggregate the positive instances per day at the county level and detect as an event any county/day pair with an unusually high count (as compared to the recent past).

Objective

In this work we investigate the extent to which social media, in particular Twitter, can be used to detect an outbreak of a disease or illness. We term these outbreaks “events”, and we will describe methodologies for detecting events.

Submitted by teresa.hamby@d… on

Despite numerous successes in using social media to detect food borne illness and to predict influenza trends, the use of social media as a public health tool has yet to gain widespread adoption. While social media data cannot directly diagnose illness, aggregate trends in symptom proliferation may readily be observed. Such trends may allow a health agency to watch for signs and symptoms related to target conditions within its jurisdiction. Further, social media surveillance offers a distinct advantage in immediacy and sensitivity as it is not dependent upon infected individuals seeking care for reportable illnesses and as such information is not delayed by the handling, transfer, and processing of reports. These advantages may enable the earlier preparation and initiation of scaled response sequences during public health emergencies. Such data may also yield additional evidence through shared symptoms, rumors, and observations crucial to an epidemiological investigation.

Objective

To formally introduce ChatterGrabber, an open source, natural language processing based toolset for public health social media surveillance. ChatterGrabber is designed to collect and categorize a high volume of content at a low cost, providing a readily deployable solution for Epidemiologists to track emergent outbreaks in the field and a signal for syndromic surveillance.

 

Submitted by Magou on
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