Research on hot news discovery model based on user interest and topic discovery

Li, Jianting; Ma, Xiao

doi:10.1007/s10586-018-1880-1

Cited by 4 publications

(3 citation statements)

References 7 publications

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“…e emergence of TDT promotes the discovery and tracking of new events in news reports [7,8]. e existing topic detection methods mainly focus on the methods based on machine learning [9][10][11][12][13].…”

Section: Related Workmentioning

confidence: 99%

ECANP: A Topic Influence Evaluation Model for Hot Topics

Chang

Zhi-yuan

Luo

2022

Computational Intelligence and Neuroscience

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Social network is an important product of industrial society. In recent years, the research related to hot topics has focused on topic detection, topic trend prediction, and topic tracking. However, the important role of topic influence evaluation in hot topic research has not received enough attention, which leads the problem of inaccurate influence calculation. In order to solve the above problems, this paper proposes a novel model to evaluate the real-time relative influence of topics in social network. The proposed model can quantify the influence of topics, and some influential factors which determine topic hotness will be analyzed and identified. In this model, five impact indicators are defined, namely user engagement, topic coverage, topic activity, topic persistence, and topic novelty to consider the topic characteristics more finely. Moreover, the proposed model not only consider traditional simple factors of like, forward and comments, but also pay attention to the relative influence and time attenuation characteristics of the topics. Further, the experimental results show that our method could quickly aggregate the influence factors of hot topics and accurately provide the influence indicator of topics.

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Section: Related Workmentioning

confidence: 99%

ECANP: A Topic Influence Evaluation Model for Hot Topics

Chang

Zhi-yuan

Luo

2022

Computational Intelligence and Neuroscience

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“…Liu et al [22] proposed a hot topic detection and tracking model TDT_CC to track the heat of a topic in real time. Zhong et al [23] detected the text topic by clustering the topic tag words, and evaluated the topic heat in combination with the internal and external characteristics of the text. Zhu et al [24] designed a two-layer network model MSBN based on feature co-occurrence and semantic community division to detect sub-topics in microblog text.…”

Section: Hot Topic Discoverymentioning

confidence: 99%

Hot Topic Discovery across Social Networks Based on Improved LDA Model

2021

KSII TIIS

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With the rapid development of Internet and big data technology, various online social network platforms have been established, producing massive information every day. Hot topic discovery aims to dig out meaningful content that users commonly concern about from the massive information on the Internet. Most of the existing hot topic discovery methods focus on a single network data source, and can hardly grasp hot spots as a whole, nor meet the challenges of text sparsity and topic hotness evaluation in cross-network scenarios. This paper proposes a novel hot topic discovery method across social network based on an im-proved LDA model, which first integrates the text information from multiple social network platforms into a unified data set, then obtains the potential topic distribution in the text through the improved LDA model. Finally, it adopts a heat evaluation method based on the word frequency of topic label words to take the latent topic with the highest heat value as a hot topic. This paper obtains data from the online social networks and constructs a cross-network topic discovery data set. The experimental results demonstrate the superiority of the proposed method compared to baseline methods.

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“…Vaca et al [20] introduced a novel framework inspired from Collective Factorization for online topic discovery able to connect topics between different time-slots. Li et al [21] proposed a double-layer text clustering model based on density clustering strategy and Single-pass strategy, to find a way to process network data and discover hot news based on a user's interest and topic. Liu [22] proposed an effective algorithm to detect and track hot topics based on chains of causes (TDT_CC), which can be used to track the heat of a topic in real time.…”

Section: Existing Research Topic Discoverymentioning

confidence: 99%

Hot Topic Community Discovery on Cross Social Networks

2019

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The rapid development of online social networks has allowed users to obtain information, communicate with each other and express different opinions. Generally, in the same social network, users tend to be influenced by each other and have similar views. However, on another social network, users may have opposite views on the same event. Therefore, research undertaken on a single social network is unable to meet the needs of research on hot topic community discovery. “Cross social network” refers to multiple social networks. The integration of information from multiple social network platforms forms a new unified dataset. In the dataset, information from different platforms for the same event may contain similar or unique topics. This paper proposes a hot topic discovery method on cross social networks. Firstly, text data from different social networks are fused to build a unified model. Then, we obtain latent topic distributions from the unified model using the Labeled Biterm Latent Dirichlet Allocation (LB-LDA) model. Based on the distributions, similar topics are clustered to form several topic communities. Finally, we choose hot topic communities based on their scores. Experiment result on data from three social networks prove that our model is effective and has certain application value.

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Research on hot news discovery model based on user interest and topic discovery

Cited by 4 publications

References 7 publications

ECANP: A Topic Influence Evaluation Model for Hot Topics

ECANP: A Topic Influence Evaluation Model for Hot Topics

Hot Topic Discovery across Social Networks Based on Improved LDA Model

Hot Topic Community Discovery on Cross Social Networks

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