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== Citation == Adam Sadilek, Henry Kautz, Vincent Silenzio "Modeling Spread of Disease from Social Interaction" Sixth AAAI International Conference on Weblogs and Social Media (ICWSM) == Online Version == [http://www.cs.rochester.edu/u/kautz/papers/Sadilek-Kautz-Silenzio_Modeling-Spread-of-Disease-from-Social-Interactions_ICWSM-12.pdf Online Pdf] == Summary == A nice [[Category::Paper]] which tries to model the spread of communicable diseases via analysis of social media. (Problem similar to [[AddressesProblem::Undetected epidemic disease spreading ]]) An analogy (from the paper!) : Given that five of your friends have flu-like symptoms, and that you have recently met eight people, possibly strangers, who complained about having runny noses and headaches, what is the probability that you will soon become ill as well? Traditionally public health is monitored via surveys and statistics obtained from health care centres. This process is expensive and slow (also biased as many of us dont even bother to take medication for flu). This work considers the fine grained interactions between individuals via their tweets. One of the main challenges of this work was to identify correctly the very small number of tweets which are related to sickness. They develop a SVM classifier for this task which performs really well (with a 0.98 precision and 0.97 recall) == Data == The work was done based on analyzing tweets which were (collected for a month) from the NYC metropolitan area. The specifics of the data are shown in the following figure : [[File:54.jpg]] == Methodologies and models == ==== Detecting illness related tweets ==== The major challenge of this work was to detect the tweets of a person which were related to illness since for every health related tweet there were more than 1000 unrelated ones. Given this class imbalance this work formulates a semi-supervised cascade based approach to learn a robust [[UsesMethod::Support Vector Machines]] (SVM). To achieve this (extract specific tweets), they first try to obtain high quality training data to train their final classifier. The following figure shows their methodology [[File:55.jpg]] To explain it briefly, they first train two different binary SVM classifier . The classifier <math> C_{S} </math> is penalized severely for false positives (normal tweets which are labelled as sick) and <math> C_{O} </math> is penalized severely for false negatives. Then the classifiers are trained by a corpus of hand labeled 5128 tweets. After this, they trained the classifiers with 1.6 million (health related though with noise) tweets which were obtained from the work by [http://www.cs.jhu.edu/~mdredze/publications/2011.tech.twitter_health.pdf Paul and Dredze]. <math> C_{O} </math> was further trained with a training set of 200 million tweets. Thresholding was applied to reduce the noise in the cascade. A final corpus with over 700 thousand “sick” messages and 3 million “other” tweets were obtained which were used as a training set for the final classifier. The features for the classifier are unigram, bigram and the trigram models (in-general [[UsesMethod::N-grams]]. ==== Modeling the spread of disease ==== This work considers two factors primarily the location proximity and the social relationship. They consider two individuals co-located if they visit the same 100 by 100 meter cell within a time window. Twitter has this concept of followers and followee. They consider two individuals to be "friends" if they follow each other. == Experiments and Results == [[File:51.jpg]] The above figure show the impact of co-location and friendship with infected people on a given day on one’s health the following day. All of them exhibit exponential relationship between probable encounter and ensuing sickness. Also larger the slack smaller the effect. The effect of friendship also has a exponential effect over the number of friends. An interesting result is present by the green color line which represent the effect of being sick if ones friends are sick but they are not co-located. This clearly shows that colocation plays an important role. == Related work and papers == Some related and interesting papers which does similar work of modeling public health from social media are [http://www.cs.jhu.edu/~mdredze/publications/2011.tech.twitter_health.pdf 1], [http://www.cs.jhu.edu/~mdredze/publications/twitter_health_icwsm_11.pdf 2]. [http://www.google.org/flutrends/ Google Flu Trends] is an interesting project for detecting flu trends around the world and is sometimes used as a metric/standard to compare performances. == Study Plan == You might want to revise *[http://en.wikipedia.org/wiki/Support_vector_machine SVM] *[http://en.wikipedia.org/wiki/N-gram N-grams] and could study the related papers in the above section (though not necessary to understand this paper!)

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