# You Are What You Tweet: Analyzing Twitter for Public Health

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3 We paired each article with an ATAM ailment and placed a prior distribution over the ailment s words based on the article. Following the LDA framework, the priors over our multinomial word distributions are defined by the Dirichlet distribution the multinomials associated with each ailment are sampled from a different Dirichlet. The Dirichlet distribution can be defined with two parameters, a mean m, which can be thought of as the most likely multinomial to be drawn from this Dirichlet, and a precision s, which controls how much a sample multinomial vector can deviate from the mean the lower the precision, the less influence the prior has over the inferred posterior. We set the mean m a as the distribution over words in the articles for the ailment a. We optimize s to maximize data likelihood using the fixed-point iteration derived by Minka (2003) to update the precision given a fixed mean. The definition of the mean and the update rule for the precision are: m a,w = cw a + λ c (1) a + λw s new w a = s m a,wψ(n w a + s a m a,w ) m a,w Ψ(s a m a,w ) a w Ψ(n a + s a ) Ψ(s a ) (2) The mean m a,w is a fixed constant, where c w a represents the count of word w in the article about ailment a. We use add-λ smoothing (with a small factor λ = 0.01) to ensure non-zero values for all words. The precision s a is updated throughout the sampling process. We use a Gibbs EM procedure in which we perform 10 iterations of sampling with fixed Dirichlet parameters, then following Eq. (2) we update s based on the current state of the sampler, where n w a is the count of word w assigned to ailment distribution a. We ran this model with Z = 20 and A = 20 using the same hyperparameters as in the unsupervised setting. We call this new model ATAM+. Evaluation We begin with a description of data and a more traditional evaluation of model output. We start with a collection of over 2 billion tweets collected from May 2009 to October 2010 (O Connor et al. 2010), from which we select health related tweets for ATAM training. Culotta (2010a) found simple keyword matching insufficient for filtering tweets (e.g., I m sick of this and justin beber ur so cool and i have beber fever ). Paul and Dredze (2011) use 5,128 labeled messages to train a high precision SVM binary classifier (0.90) to identify health related messages, which produced a corpus of 1.63 million English tweets. We removed punctuation, stop words, URLs and hashtags. ATAM (fully unsupervised) and ATAM+ (using WebMD articles as prior knowledge) were trained over these health messages. Ailment output was annotated by two people not involved in running the models or aware of the WebMD articles. Each person labeled the 20 ailments with disease names of their own choosing or as incoherent based on the top 20 words for the general, symptom, and treatment distributions. For the 20 ailments, annotators agreed on labels for 17 ATAM ailments (of which 4 were incoherent). ATAM+ produced more clear and coherent ailments; annotators agreed on 15 ailments (all coherent) and no cluster was identified by both annotators as incoherent. Examples of seven of the fifteen ATAM+ ailments with annotator labels appear in Figure 1. The remaining ailments were respiratory illness, common cold, infections, physical injuries, headaches, exercise, skin problems and insomnia. To improve interpretability of this table, we include longer phrases in addition to single words. For each contiguous sequence of tokens in a tweet assigned to the same ailment distribution, we extracted a single phrase and included it in the ailment distributions. Several high frequency phrases appear in the top of the ailment distributions. The discovered ailments qualitatively match their WebMD priors (first row of Figure 1), but the discovered ailments clusters often are more general. For example, the ailment with a foot injuries prior produced a general injuries ailment; knee, shoulder, and arm were top general words, crutches and physical therapy were top treatments. Likewise, the breast cancer prior resulted in general cancer; while breast cancer is the most frequent symptom, stomach, lung, and skin cancer all appear as top 10 symptom phrases. Additionally, we observe some confusions in the output between ailments with similar treatments or symptoms. For example, the cancer ailment also includes symptom phrases of pneumonia and heart attack, and heart surgery is the top treatment phrase. These are all serious illnesses that appear within similar contexts (words like pray and recovery ) which caused the model to cluster them together. In general, this problem occurred more without the prior knowledge used in ATAM+. For example, allergies would appear with the skin problems ailment in ATAM, but ATAM+ kept allergic reactions (e.g. hives and rashes) separate from seasonal allergies. Comparison to Public Health Articles To evaluate the output interpretability, we compare the ailment distributions with distributions estimated from WebMD articles the same 20 articles used as priors. To separately evaluate the symptom and treatment distributions, we filtered each article for symptoms and treatments using the keyphrase lists, then built ailment specific uni-gram language models. We then evaluate how well our ailment clusters match the WebMD articles by using these distributions to align the ATAM and ATAM+ ailments with the articles. Each article was paired with its corresponding ailment in the model output, as labeled by the annotators these pairings serve as our gold standard alignments though not all articles had a corresponding ailment and vice versa. The Jenson-Shannon (JS) divergence was computed between the distributions for each ailment and the distributions for each WebMD disease. To account for ailments that contained very common symptoms and treatments, we measure the standard score: x x σ of the JS divergence, where x and σ are the mean and standard deviation of the divergence. This score was then used to define a ranking of articles for ailments, and ailments for articles. Ranking quality was measured as the percentage of articles/ailments such that the highest-ranked alignment was the correct alignment, as well as with mean 1 reciprocal rank (MRR), i.e. the mean of rank, where rank is the position of the correct article/ailment in the list. Results

6 watery helsinki mold watering faucet lolss sneezes sneezy teary bloom pollen zyrtec claritin spring watering trees watery itching bloom grass (a) February (b) April hayfever grass watering watery claritin humidity zyrtec bonkers mold antihistamines dust catherine pinche buildings mold hadd gato cessation meditating ragweed (c) June (d) August Figure 3: Rates of allergies discovered by ATAM+ by state for four months in Darker shading indicates higher allergy rates; diagonal shaded states lacked sufficient data. Notice that allergy rates peak in the middle of the allergy season (April), and that in colder weather (February) they are more prominent in warmer climates (West and South). Next to each month are the top 10 words w most predictive of the allergy ailment a during the month m (p(a, m w)). Bolded words are those especially recognized as being associated with particular allergy seasons, e.g. ragweed begins to pollinate in mid-august, tree pollen season is in full effect in April, and June is often the worst month for hay fever ( the rest of the country by April, the peak of the season. The primary allergen in the spring is tree pollen, indicated by the presence of pollen and trees in April. Grass allergies peak in May and June; observe that grass is one of the top words in June. Weed season begins in August, the main contributor of which is ragweed, which is known to be especially prevalent in the Midwest. Notice that ragweed is associated with August, and that many Midwestern states have the highest rate during this month. We seek quantitative validation of trends by time and location. Returning to the task of influenza tracking, the CDC provides infection rates by state. 10 We compute the influenza rates using the flu ailment as before, but now compute separate rates for each state. We measured the rates for the influenza season (12 months from September). The CDC data uses a numeric value (integer between 1 and 10) indicating the level of flu activity for each week in each state. We ignored state/week pairs where the total number of tweets was less than 50, reducing the number of data points to 39% of the total, yielding 1057 data points. ATAM+ obtained a correlation of 0.66, and ATAM yielded These correlations demonstrate the feasibility of syndromic surveillance within specific geographic regions Word Ent. Most Common Ailments vomiting 2.19 Flu (23%), Aches (16%), Insomnia (12%) burn 2.02 Skin (36%), Aches (17%), Headache (2%) chill 1.95 Headache (28%), Insomnia (18%), Flu (12%) fever 1.46 Flu (50%), Cold (24%), Infection (11%) pimples 0.72 Skin (84%), Depression (5%) fractured 0.69 Physical injuries (82%), Cancer (12%) toothache 0.61 Dental (83%), Insomnia (9%), Aches (6%) headache 0.56 Headache (75%), Insomnia (25%) tumor 0.22 Cancer (96%) mood 0.20 Depression (96%), Obesity (4%) Table 3: Common symptom words, their most commonly associated ailments, and the entropy of their distribution over ailments. Entropy values ranged from 0 to Analyzing Symptoms and Medications Discovered ailments include both milder, acute illness (influenza, allergies, etc.) as well as more common chronic conditions (obesity and insomnia). For many of these ailments, sufferers often do not visit their doctor, instead managing the illness on their own. Flu patients often stay home, take medication and eat soup. Obese people put themselves on a diet and exercise regimen. Insomnia suffers resort to changing their sleeping environment or sleeping aids. Since people are not seeing health care providers, their illness, symptoms and chosen treatments go unreported. Obtaining these statis-

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