An Effective Approach for Modelling Time Features for Classifying Bursty Topics on Twitter

Fang, Anjie; Ounis, Iadh; Macdonald, Craig; Habel, Philip; Xiong, Xiaoyu; Yu, Haitao

doi:10.1145/3269206.3269253

Cited by 3 publications

(2 citation statements)

References 15 publications

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“…A detailed survey on popularity prediction techniques is provided in [13]. Twitter has also seen its fair share of popularity prediction methods for hashtags [11] and tweets [33]. [38] proposed a technique to predict the vulnerability of a CVE (Common Vulnerabilities and Exposures) code based on tweets.…”

Section: Popularity Predictionmentioning

confidence: 99%

GitEvolve: Predicting the Evolution of GitHub Repositories

Zhou¹,

Ravi²,

Muñiz³

et al. 2020

Preprint

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Software development is becoming increasingly open and collaborative with the advent of platforms such as GitHub. Given its crucial role, there is a need to better understand and model the dynamics of GitHub as a social platform. Previous work has mostly considered the dynamics of traditional social networking sites like Twitter and Facebook. We propose GitEvolve, a system to predict the evolution of GitHub repositories and the different ways by which users interact with them. To this end, we develop an end-to-end multi-task sequential deep neural network that given some seed events, simultaneously predicts which user-group is next going to interact with a given repository, what the type of the interaction is, and when it happens. To facilitate learning, we use graph based representation learning to encode relationship between repositories. We map users to groups by modelling common interests to better predict popularity and to generalize to unseen users during inference. We introduce an artificial event type to better model varying levels of activity of repositories in the dataset. The proposed multi-task architecture is generic and can be extended to model information diffusion in other social networks. In a series of experiments, we demonstrate the effectiveness of the proposed model, using multiple metrics and baselines. Qualitative analysis of the model's ability to predict popularity and forecast trends proves its applicability 1 . CCS CONCEPTS• Information systems → Social networks; • Computing methodologies → Neural networks; • Human-centered computing → Social networks.

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Section: Popularity Predictionmentioning

confidence: 99%

GitEvolve: Predicting the Evolution of GitHub Repositories

Zhou¹,

Ravi²,

Muñiz³

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…Yan et al [20] proposed the BTM model based on the traditional topic model LDA, then Yan et al [19] improved this model and proposed the BBTM model. Fang et al [23] adopted temporal features to model classification problems for outbreak topics in Twitter, Hammad and El-Beltagy [24] automatically performed burst feature detection from Twitter streams for online topic extraction. Last, in the graph analysis, Katsurai et al [21] used the dynamic co-occurrence word network to mine the topic from the academic data, Ma et al [22] applied the graph-based analysis of natural disaster theme mining to discover hot topics.…”

Section: B Hot Topic Detectionmentioning

confidence: 99%

Detecting Hot Topics From Academic Big Data

et al. 2019

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Detecting hot topics from massive academic data is a very challenging task. Because various types of academic information are overgrowing, e.g., papers, news, and blogs, which has gone far beyond the limits that researchers can accept. Therefore, how to efficiently and accurately detect hot topics from big academic data is the main problem that researchers are facing. In view of this, we design a general framework for Academic Hot Topic Detection (AHTD). Specifically, in this framework, a DeepWalk-based keyword extraction algorithm for a single paper (S-DWKE) is proposed to detect popular topics in diverse academic fields dynamically. Moreover, we propose a keyword extraction algorithm to extract keywords from multiple articles (M-GCKE), which enables us to detect new topics in emerging academic areas. Then, hot topics can be generated from keywords extracted by the S-DWKE and M-GCKE. A large number of experiments demonstrates the proposed framework effectively improves the performance of hot topic detection in the academic field and performs better than the comparison algorithms. We have applied the above work to the ''Academic Headline'' application to provide the hot topics for researchers. INDEX TERMS Academic big data, hot spot discovery, keyword extraction, knowledge representation.

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Analysing political events on Twitter

Fang

2019

SIGIR Forum

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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' 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 [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 of topics 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 discussed and '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 the time 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. Next, to identify 'who' participated in the topic discussions, we propose approaches to identify 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 for classifying 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 the connections and dynamics among communities on Twitter. Supervisors : Iadh Ounis, Craig Macdonald, Philip Habel The thesis is available at http://theses.gla.ac.uk/41135/

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An Effective Approach for Modelling Time Features for Classifying Bursty Topics on Twitter

Abstract: There may be differences between this version and the published version. You are advised to consult the publisher's version if you wish to cite from it.

Cited by 3 publications

References 15 publications

GitEvolve: Predicting the Evolution of GitHub Repositories

GitEvolve: Predicting the Evolution of GitHub Repositories

Detecting Hot Topics From Academic Big Data

Analysing political events on Twitter

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