Short text topic modelling approaches in the context of big data: taxonomy, survey, and analysis

Murshed, Belal Abdullah Hezam; Suresha, M; Abawajy, Jemal; Saif, Mufeed Ahmed Naji; Al-ariki, Hasib Daowd Esmail; Abdulwahab, Hudhaifa Mohammed

doi:10.1007/s10462-022-10254-w

Cited by 38 publications

(16 citation statements)

References 262 publications

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“…On the other hand, topic extraction, or topic modeling, is defned as the technique used to infer conceptual topics hidden in a set of documents, or corpus [22,24], where there is no explicit taxonomic scheme to project onto a corpus or when such projection (or labeling) is costly [25]. In another defnition, topic modeling is an automated process to defne the "latent thematic structure" of a corpus, summarizing the texts into topics or categorizing them into labels [26].…”

Section: Methodsmentioning

confidence: 99%

Conceptualizing Discussions on the Dark Web: An Empirical Topic Modeling Approach

Basheer,

Alkhatib

2024

Complexity

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Social networks on the Internet have become a home that attracts all types of human thinking to exchange knowledge and ideas and share businesses. On the other hand, it has also become a source for researchers to analyze this knowledge and frame it in patterns that define types of thoughts circulating on these networks and representing the communities around them. In particular, some social networks on the Dark Web attract a special kind of thinking centered around the malicious and illegal activities disseminated on websites and marketplaces on the Dark Web. These networks involve discussions to exchange and discourse information, tips, and advice on performing such business. Studying social networks on the Dark Web is still in its infancy. In this paper, we present a methodology for analyzing the content of social networks on the Dark Web using topic modeling methods. We demonstrate the needed stages for the topic modeling process, beginning with data preprocessing and feature extraction to topic modeling algorithms. We utilize and discuss the following four topic models: LDA, CTM, PAM, and PTM. We discuss the following four topic coherence measures as evaluation metrics: UMass, UCI, CNPMI, and CV, demonstrating the selection of the best number of topics for each model according to the most coherent produced topics. Furthermore, we discuss the limitations, challenges, and future work. Our proposed approach highlights the ability to discover the latent thematic patterns in conversations and messages in the common language used in social networks on the Dark Web, constructing topics as groups of terms and their associations. This paper provides researchers with a leading methodology for analyzing thought patterns on the Dark Web.

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Section: Methodsmentioning

confidence: 99%

Conceptualizing Discussions on the Dark Web: An Empirical Topic Modeling Approach

Basheer,

Alkhatib

2024

Complexity

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“…Many natural language processing, machine learning, and topic modeling techniques have been developed to uncover information from unstructured data (Murshed et al, 2023 ). Most of these techniques require programming skills, but improvements in the user-friendly coding software have enabled their widespread use (Yu and Egger, 2021 ).…”

Section: Introductionmentioning

confidence: 99%

Topic modeling and social network analysis approach to explore diabetes discourse on Twitter in India

Ramamoorthy,

Kulothungan,

Mappillairaju

2024

Front. Artif. Intell.

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IntroductionThe utilization of social media presents a promising avenue for the prevention and management of diabetes. To effectively cater to the diabetes-related knowledge, support, and intervention needs of the community, it is imperative to attain a deeper understanding of the extent and content of discussions pertaining to this health issue. This study aims to assess and compare various topic modeling techniques to determine the most effective model for identifying the core themes in diabetes-related tweets, the sources responsible for disseminating this information, the reach of these themes, and the influential individuals within the Twitter community in India.MethodsTwitter messages from India, dated between 7 November 2022 and 28 February 2023, were collected using the Twitter API. The unsupervised machine learning topic models, namely, Latent Dirichlet Allocation (LDA), non-negative matrix factorization (NMF), BERTopic, and Top2Vec, were compared, and the best-performing model was used to identify common diabetes-related topics. Influential users were identified through social network analysis.ResultsThe NMF model outperformed the LDA model, whereas BERTopic performed better than Top2Vec. Diabetes-related conversations revolved around eight topics, namely, promotion, management, drug and personal story, consequences, risk factors and research, raising awareness and providing support, diet, and opinion and lifestyle changes. The influential nodes identified were mainly health professionals and healthcare organizations.DiscussionThe study identified important topics of discussion along with health professionals and healthcare organizations involved in sharing diabetes-related information with the public. Collaborations among influential healthcare organizations, health professionals, and the government can foster awareness and prevent noncommunicable diseases.

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“…Several terms, such as slang and abbreviations often seen in tweets, are absent from dictionaries (Kolajo et al, 2022). • Short and unstructured tweets: This issue makes it difficult to interpret the semantics of short informal writings on Twitter (Lilleberg et al, 2015;Murshed et al, 2023) . The shortness of tweets sometimes results in inadequate context, making determining a post's mood or genuine intention difficult.…”

Section: Introductionmentioning

confidence: 99%

Distilbert-gnn: a Powerful Approach to Social Media Event Detection

Abagissa,

Saxena,

Chandra

2024

Preprint

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Finding events actively discussed locally or globally is a significant problem when mining social media data streams. Identifying such events can serve as an early warning system in an event such as an accident, a protest, an election, or other breaking news. However, with the massive volume of social media feeds streaming, early detection of such events is inherently complex. Despite the advances in social media event detection, existing methods often struggle with the dynamic nature of social media, the volume and velocity of data, and the ambiguity in user-generated content. On the contrary, several relational aspects are present in social media that, if suitably handled and exploited, can improve detection performance. To mitigate these challenges, we propose "DistilBERT-GNN," an incremental event detection framework that leverages DistilBERT and Graph Neural Networks (GNNs). By integrating DistilBERT's real-time contextual understanding with GNNs' ability to capture evolving relationships in social media networks, our framework aims to detect and track events as they emerge and evolve. We assess the effectiveness of our approach through comparative analysis against various state-of-the-art event detection methods on a real-world Twitter dataset. Our experimental result demonstrates that DistilBERT-GNN outperforms the baselines with NMI, AMI, and ARI metrics by 0.72, 0.53, and 0.24, respectively.

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Short text topic modelling approaches in the context of big data: taxonomy, survey, and analysis

Cited by 38 publications

References 262 publications

Conceptualizing Discussions on the Dark Web: An Empirical Topic Modeling Approach

Conceptualizing Discussions on the Dark Web: An Empirical Topic Modeling Approach

Topic modeling and social network analysis approach to explore diabetes discourse on Twitter in India

Distilbert-gnn: a Powerful Approach to Social Media Event Detection

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