# Collecting a Large Scale Dataset for Classifying Fake News Tweets Using Weak Supervision
## 4 Approach
### Topic Features  
Since the topic of a tweet may have a direct influence on the fake news classification, as some topics are likely more prone to fake news than others, we also apply topic modeling for creating features from the tweets. Again, we train topic models using the _gensim_ library.  
_Latent Dirichlet Allocation (LDA)_ is a three-level hierarchical Bayesian model of a set of documents. Each document is modeled as a mixture of a set of \(K\) topics. In turn, each topic is modeled through a distribution over words [33]. The number of topics, i.e., \(K\), is a user defined number. In a nutshell, the algorithm first assigns a random topic to each word in the corpus and then iteratively improves these guesses based on the probability for a topic, given a word and a document until a convergence criteria is met. As a simplified example, assume that LDA was configured to discover two topics \(t_{0}\) and \(t_{1}\) from ten documents, i.e., \(K=2\). The output of LDA is then, for instance, that the first document is 80% about topic \(t_{0}\), whereas another document is 40% about \(t_{0}\) and 40% about topic \(t_{1}\). These topics \(t_{0}\) and \(t_{1}\) are then in turn built from words in the documents. For example, topic \(t_{0}\) is built 50% from a word \(w_{0}\), 30% from a word \(w_{1}\) and so on. The LDA implementation for online learning was similar to Doc2 Vec chosen from the _gensim_ library. The advantage of using an online instead of a batch implementation is that documents can be processed as a stream and discarded after one look. Hence, documents do not need to be stored locally, which in turn is memory efficient [34].  
One of the major disadvantages of LDA is that \(K\), i.e., the number of topics is a mandatory user specified parameter. Especially when a fixed number of topics is hard to determine as it is for news tweets, setting the best value is not an easy task. The _Hierarchical Dirichlet Process (HDP)_ proposed by [35] solves this problem. It is used to generalize LDA and determines the number of clusters directly from the data. In LDA, a topic is represented by a probability distribution across a set of words and a document by a probability distribution across a finite set of topics. In the nonparametric version, also  
Figure 5: F1 scores using different dimensions for Doc2 vec, using DBOW (in blue) and DM (in red).  
referred to as HDP-LDA, the topics do not come from a finite set but they are linked, so that the same topic can appear in different documents [36]. Again, the online implementation from the _gensim_ library was used. It is inspired by the one for LDA and thus provides the same benefits. In previous research, it has been demonstrated on two text collections that the HDP outperforms the online LDA model according to the per-word likelihood [37].  
We trained both a Latent Dirichlet Allocation model (LDA) on the whole dataset, varying the number of topics between 10 and 200 in steps of 10, as well as a Hierarchical Dirichlet Process (HDP) model. As for the text representations with BOW and doc2 vec, we conducted a prestudy with different classifiers in their standard configurations, and cross validation on the training set. As shown in Figure 6, LDA constantly outperformed HDP, but the number of topics at which the best results are achieved again varies between learners.