## 4.5.1 Automatic classification 

Representations We evaluated a number of different representations of tweet content. Here we present three which overall produced the most interesting results. These are variations on well-known representations in the existing literature, adapted to the problem at hand. Specifically, we report on the performance of the following: 

– binary bag of terms (pre-processed words) [43, 44], 

– integer bag of terms (also referred to as term count) [45], and 

– tf-idf (term frequency-inverse document frequency) score [46]. 

In the binary bag of terms representation used in this work, each entry x$_{i}$ in a feature vector x$_{bow}$ = [ x$_{1}$, x$_{2}$, . . . , x$_{n}$$_{w}$ ] T corresponds to a particular term and is coded as either present in a particular tweet (value 1) or absent from it (value 0), where x$_{bow}$ ∈$_{R}$ n$_{d}$ and n$_{d}$ is the dictionary size over which the feature vector is constructed (we will discuss vocabulary construction in more detail shortly). Thus, the original tweet: 

Looks like we will have more #autism research happening for children in #EarlyIn- tervention next year! :-) #VisualSupports #MobileTech 

which following our pre-processing ends up as: 

look like autism research happen child in earlyintervention visualsupports mobiletech 

is represented using the following binary bag of words feature vector: 

x$_{bow}$ = [ 0 . . . look ︷︸︸︷ 1 0 . . . like ︷︸︸︷ 1 0 . . . research ︷︸︸︷ 1 0 . . . 0 happen ︷︸︸︷ 1 0 . . . child ︷︸︸︷ 1 0 . . . 0$^{]}$$^{T}$ 

Note that the term “autism” is not included as it is matched by one of our search keywords used for quasi-ground truth labelling, as described in Section 4.1. On the other hand, the terms “earlyintervention”, “visualsupports”, and “mobiletech” do not have the correspond- ing entries in the feature vector because their frequency across the data corpus is too low 

