Recently, political events, such as elections or referenda, have raised a lot of discussions on social media networks, in particular, Twitter. This brings new opportunities for social scientists to address social science tasks, such as understanding what communities said, identify- ing whether a community has an influence on another or analysing how these communities respond to political events online. However, identifying these communities and extracting what they said from social media data

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... e challenging and non-trivial tasks. In this thesis, we aim to make progress towards understanding 'who' (i.e. communities) said 'what' (i.e. discussed topics) and 'when' (i.e. time) during political events on Twitter. While identifying the 'who' can benefit from Twitter user community classification approaches, 'what' they said and 'when' can be effectively addressed on Twitter by extracting their discussed topics using topic modelling approaches that also account for the importance of time on Twitter. To evaluate the quality of these topics, it is necessary to investigate how coherent these topics are to humans. Accordingly, we propose a series of approaches in this thesis. First, we investigate how to effectively evaluate the coherence of the topics generated using a topic modelling approach. The topic coherence metric evaluates the topical coherence by examining the semantic similarity among words in a topic. We argue that the semantic similarity of words in tweets can be effectively captured by using word embeddings trained using a Twitter background dataset. Through a user study, we demonstrate that our proposed word embedding-based topic coherence metric can assess the coherence of topics like humans. In addition, inspired by the precision at k information retrieval metric, we propose to evaluate the coherence of a topic model (containing many topics) by averaging the top-ranked topics within the topic model. Our proposed metrics can not only evaluate the coherence of topics and topic models, but also can help users [...]