24 Adham Beykikhoshk, Ognjen Arandjelovi´c, Dinh Phung, Svetha Venkatesh, and Terry Caelli i.e. they are not amongst the most frequent terms included in our vocabulary. Lastly, “in” is excluded (automatically, as all others) on the basis that it is too short. The term count representation is similar to the binary bag of words described previously. As before each entry in a feature vector corresponds to a particular term. However, unlike before the entries are not binary variables which signify the presence or the absence of a par- ticular term but rather actual counts of the term’s instances in a tweet. Since the previously given example of a tweet does not include any repeated terms, the term count feature vector x$_{tc}$ ∈ R n$_{d}$ in this instance is identical to x$_{bow}$ . On the other hand, the tweet mentioned in Section 4.1.1 which after pre-processing looks as follows: 101 autism genet analysi individu autism find gene delet use power genet sequenc url produces different bag of words and term count feature vectors, the former having the value of 1 for the entry corresponding to the term “genet” and the latter the value of 2 since the term has two occurrences in the tweet. Lastly, the tf-idf representation x$_{tfidf}$ ∈$_{R}$ n$_{d}$ too has entries which correspond to differ- ent vocabulary terms, but with values which measure the importance of a particular term in a tweet. Specifically, a particular entry is equal to the frequency of the corresponding term (‘term frequency’) weighted by the inverse number of tweets in the training corpus which also contain the term (‘document frequency’). This representation has been used success- fully in a variety of applications, from text mining [46] to visual object recognition and retrieval [47]. Vocabulary construction We also explored a number of different ways of selecting the vo- cabulary over which tweet feature vectors are constructed based on the discriminativeness of terms, as well as their group-based or combined frequencies of use. Specifically, we exam- ined vocabularies formed by choosing the most frequent terms in one of the two sub-groups, the most discriminative terms in one of the two sub-groups (as explained in Section 4.5 and visualised in Fig. 8), the union or the intersection of either of the former (e.g. the union of the most discriminative terms in the ASD sub-group and the most discriminative words in the