DOCTORAL ABSTRACT

Analysing Political Events on Twitter: TopicModelling and User Community Classification
Anjie Fang
Glasgow, UKa.fang.1@research.gla.ac.uk

Abstract
Recently, political events, such as elections, 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 or identifying
whether a community has an influence on another. However, identifying these communities
and extracting what they said from social media data are challenging and non-trivial tasks.
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’ theysaid 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 coherenceby 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 trainedusing 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 likehumans [1, 2]. In addition, inspired by the precision at k metric, we propose to evaluate
the coherence of a topic model (containing many topics) by averaging the top-ranked topics
within the topic model [3]. Our proposed metrics can not only evaluate the coherence oftopics and topic models, but also can help users to choose the most coherent topics.
Second, we aim to extract topics with a high coherence from Twitter data. Such topics
can be easily interpreted by humans and they can assist to examine ‘what’ has been discussedand ‘when’. Indeed, we argue that topics can be discussed in different time periods (see [4])and therefore can be effectively identified and distinguished by considering their time periods.
Hence, we propose an effective time-sensitive topic modelling approach by integrating thetime dimension of tweets (i.e. ‘when’) [5]. We show that the time dimension helps to generate
topics with a high coherence. Hence, we argue that ‘what’ has been discussed and ‘when’
can be effectively addressed by our proposed time-sensitive topic modelling approach.

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Next, to identify ‘who’ participated in the topic discussions, we propose approaches toidentify the community affiliations of Twitter users, including automatic ground-truth generation approaches and a user community classification approach. We show that the mentioned
hashtags and entities in the users’ tweets can indicate which community a Twitter user belongs to. Hence, we argue that they can be used to generate the ground-truth data forclassifying users into communities. On the other hand, we argue that different communities
favour different topic discussions and their community affiliations can be identified by leveraging the discussed topics. Accordingly, we propose a Topic-Based Naive Bayes (TBNB)
classification approach to classify Twitter users based on their words and discussed topics [6]. We demonstrate that our TBNB classifier together with the ground-truth generation
approaches can effectively identify the community affiliations of Twitter users.
Finally, to show the generalisation of our approaches, we apply our approaches to analyse
3.6 million tweets related to US Election 2016 on Twitter [7]. We show that our TBNB
approach can effectively identify the ‘who’, i.e. classify Twitter users into communities. To
investigate ‘what’ these communities have discussed, we apply our time-sensitive topic modelling approach to extract coherent topics. We finally analyse the community-related topics
evaluated and selected using our proposed topic coherence metrics.
Overall, we contribute to provide effective approaches to assist social scientists towards
analysing political events on Twitter. These approaches include topic coherence metrics,
a time-sensitive topic modelling approach and approaches for classifying the community
affiliations of Twitter users. Together they make progress to study and understand theconnections and dynamics among communities on Twitter.
Supervisors: Iadh Ounis, Craig Macdonald, Philip Habel
The thesis is available at http://theses.gla.ac.uk/41135/

References[1] Anjie Fang, Craig Macdonald, Iadh Ounis, and Philip Habel. Topics in tweets: A user
study of topic coherence metrics for Twitter data. In Proc. of ECIR, 2016.
[2] Anjie Fang, Craig Macdonald, Iadh Ounis, and Philip Habel. Using word embedding toevaluate the coherence of topics from Twitter data. In Proc. of SIGIR, 2016.
[3] Anjie Fang, Craig Macdonald, Iadh Ounis, and Philip Habel. Examining the coherenceof the top ranked tweet topics. In Proc. of SIGIR, 2016.
[4] Anjie Fang, Iadh Ounis, Craig MacDonald, Philip Habel, Xiaoyu Xiong, and Hai-TaoYu. An effective approach for modelling time features for classifying bursty topics onTwitter. In Proc. of CIKM, 2018.
[5] Anjie Fang, Craig Macdonald, Iadh Ounis, Philip Habel, and Xiao Yang. Exploring timesensitive variational bayesian inference LDA for social media data. In Proc. of ECIR,2017.
[6] Anjie Fang, Iadh Ounis, Philip Habel, Craig Macdonald, and Nut Limsopatham. Topiccentric classification of Twitter user’s political orientation. In Proc. of SIGIR, 2015.
[7] Anjie Fang, Philip Habel, Iadh Ounis, and Craig MacDonald. Votes on twitter: Assessing
candidate preferences and topics of discussion during the 2016 us presidential election.
SAGE Open, 9(1):1–17, 2019.

ACM SIGIR Forum39Vol. 53 No. 1 June 2019