TT-graph: A new model for building social network graphs from texts with time series

Wei, Jia; Ma, Ruizhe; Li, Yan; Niu, Weinan; Ma, Zongmin

doi:10.1016/j.eswa.2021.116405

Cited by 5 publications

(3 citation statements)

References 28 publications

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“…Commonly employed metrics and approaches include Precision and recall measures quantify the balance between the quality and the amount of the projected relationships. Precision is the ratio of true positives divided by the total number of positive predictions (TP + FP), whereas recall is the quotient of true positives divided by the total number of actual positives (TP + FN) (Jia et al, 2022). F1-score measures the overall quality of the predicted connections by combining precision and recall.…”

Section: Link Predictionmentioning

confidence: 99%

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Generating Attribute Similarity Graphs: A User Behavior-Based Approach from Real- Time Microblogging Data on Platform X

Hasan,

Bakar,

Yaakub

2024

Preprint

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Social network analysis is a powerful tool for understanding various phenomena, but it requires data with explicit connections among users. However, such data is hard to obtain in real-time, especially from platforms like X, commonly known as Twitter, where users share topic-related content rather than personal connections. Therefore, this paper tackles a new problem of building a social network graph in real-time where explicit connections are unavailable. Our methodology is centred around the concept of user similarity as the fundamental basis for establishing connections, suggesting that users with similar characteristics are more likely to form connections. To implement this concept, we extracted easily accessible attributes from the Twitter platform and proposed a novel graph model based on similarity. We also introduce an Attribute-Weighted Euclidean Distance (AWED) to calculate user similarities. We compare the proposed graph with synthetic graphs based on network properties, online social network characteristics, and predictive analysis. The results suggest that the AWED graph provides a more precise representation of the dynamic connections that exist in real-world online social networks, surpassing the inherent constraints of synthetic graphs. We demonstrate that the proposed method of graph construction is simple, flexible, and effective for network analysis tasks.

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Section: Link Predictionmentioning

confidence: 99%

“…F1-score measures the overall quality of the predicted connections by combining precision and recall. It is computed by averaging the values of precision and recall; it has a range of 0 to 1, with 1 representing the highest quality and 0 representing the lowest (Gui, 2024;Jia et al, 2022).…”

Section: Link Predictionmentioning

confidence: 99%

Generating Attribute Similarity Graphs: A User Behavior-Based Approach from Real- Time Microblogging Data on Platform X

Hasan,

Bakar,

Yaakub

2024

Preprint

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“…Q&A texts are relatively short. Since the question and answer are relatively short, we adopted the biterm topic model (BTM) to extract the topic sets of questions and answers (Jia et al, 2022). Furthermore, based on Yang et al (2021), Q&A topic consistency is calculated based on the Jaccard similarity coefficient.…”

Section: Explanatory Variablesmentioning

confidence: 99%

The Influencing Factors of the Helpfulness of User-Generated Product Q&As

Liang,

Li,

Wang

et al. 2023

SAGE Open

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User-generated product questions and answers (Q&As) are a new way for consumers to exchange product knowledge that can provide important support for consumer decisions. Research on the influencing factors of consumers’ judgment of the helpfulness of user-generated product Q&As can help optimize Q&A systems. Although research has extensively examined the impacts of answers and answerers on user-generated Q&As, the underlying mechanism of how the characteristics of questions and Q&As affect the helpfulness of user-generated Q&As has rarely been explored. Based on the elaboration likelihood model (ELM), a research model reflecting the impacts of the characteristics of questions and Q&As on the helpfulness of user-generated product Q&As is developed and empirically examined by using data collected from 4,814 product questions and 10,573 corresponding answers on Amazon.com. Specifically, the moderating effects of the question type and product type on the relationship between Q&A cues and the helpfulness of user-generated product Q&As are investigated. The results show that the helpfulness of user-generated product Q&As is positively affected by answer professionalism, Q&A topic consistency, answer opinion consistency, and answer valence, while it is negatively influenced by answer knowledge stickiness. As expected, the relationship between answer knowledge stickiness, answer opinion consistency, and the helpfulness of user-generated product Q&As is weakened when products are experiential. The relationship between answer knowledge stickiness, Q&A topic consistency, and the helpfulness of user-generated product Q&As is weakened when consumers’ questions are correlated with product attributes.

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Feedback2Event: Public attention event extraction from spontaneous data for urban management

Antonov,

Kontsevik,

Natykin

et al. 2023

Procedia Computer Science

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TT-graph: A new model for building social network graphs from texts with time series

Cited by 5 publications

References 28 publications

Generating Attribute Similarity Graphs: A User Behavior-Based Approach from Real- Time Microblogging Data on Platform X

Generating Attribute Similarity Graphs: A User Behavior-Based Approach from Real- Time Microblogging Data on Platform X

The Influencing Factors of the Helpfulness of User-Generated Product Q&As

Feedback2Event: Public attention event extraction from spontaneous data for urban management

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