Popularity Prediction on Online Articles with Deep Fusion of Temporal Process and Content Features

Liao, Dongliang; Xu, Jinan; Li, Gongfu; Huang, Weijie; Liu, Weiqing; Li, Jing

doi:10.1609/aaai.v33i01.3301200

Cited by 68 publications

(44 citation statements)

References 11 publications

(17 reference statements)

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“…A method to predict hot questions in Stack Overflow is studied here. According to previous works [1,11], we use the number of views to characterise whether the question is hot or not. We propose the VSAF method which uses a fully convolutional neural network to analyse the view amount changes, answer amount changes and score changes soon after questions' creation.…”

Section: Discussionmentioning

confidence: 99%

“…Threats to construct validity refers to the suitability of the evaluation metrics we use. We use Accuracy , Precision , Recall and F 1‐ score , which are also used by previous works to evaluate effectiveness of popular prediction methods [1,11]. Therefore, we believe there is little threat to construct validity.…”

Section: Threats To Validitymentioning

confidence: 99%

“…According to previous works [1,11], we use the number of views to characterise whether the question is hot or not. For a question, it is either a hot question or a cold question.…”

Section: Introductionmentioning

confidence: 99%

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Hot question prediction in Stack Overflow

2021

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Stack Overflow is a very popular programming question and answer community. Some questions become hot, and receive high views, which are of widespread concern to developers. Finding hot questions early can give priority to recommend potential hot questions to answers, thereby shortening the response time. Besides, the hot question prediction is also helpful for making advertising plan, planning advertising campaigns and estimating costs. Therefore, it is important to predict hot questions. The authors propose the VSAF method which analyses the View amount changes, Answer amount changes and Score changes soon after questions' creation based on Fully convolutional neural network. The performance of the VSAF method based on a training set and two different test sets has been evaluated. The training set has 1600 hot questions and 1600 cold questions. The random test set has 381 hot questions and 2819 cold questions, while the balanced test set has 400 hot questions and 400 cold questions. The experimental results show that using the balanced test set, VSAF achieves Accuracy, F1 hot and F1 cold of 80%, 77.77% and 81.81%, which outperforms the baseline approach by 25.59%, 21.52% and 29.04%, respectively. Using the random test set for evaluation, VSAF achieves Accuracy, F1 hot and F1 cold of 84.91%, 53.96% and 90.97%, which outperforms the baseline approach by 31.83%, 84.16% and 19.35%, respectively. The VSAF method significantly outperforms the state-of-the-art approach on hot question prediction. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Section: Discussionmentioning

confidence: 99%

Section: Threats To Validitymentioning

confidence: 99%

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Hot question prediction in Stack Overflow

2021

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“…For evaluation purposes, since there is no public benchmark dataset for our task yet, we construct a self-media online article quality classification dataset 1 from Wechat, a well-known mobile self-media platform in China, where both media organizations and personal users can set up their official accounts for publishing news and articles [12]. Our dataset covers 44 categories of articles on this platform, including news, finance, technology, people's livelihood, etc.…”

Section: Experiments 41 Datasetmentioning

confidence: 99%

Cognitive Representation Learning of Self-Media Online Article Quality

Wang

Huang

et al. 2020

Proceedings of the 28th ACM International Conference on Multimedia

Self Cite

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The automatic quality assessment of self-media online articles is an urgent and new issue, which is of great value to the online recommendation and search. Different from traditional and wellformed articles, self-media online articles are mainly created by users, which have the appearance characteristics of different text levels and multi-modal hybrid editing, along with the potential characteristics of diverse content, different styles, large semantic spans and good interactive experience requirements. To solve these challenges, we establish a joint model CoQAN in combination with the layout organization, writing characteristics and text semantics, designing different representation learning subnetworks, especially for the feature learning process and interactive reading habits on mobile terminals. It is more consistent with the cognitive style of expressing an expert's evaluation of articles. We have also constructed a large scale real-world assessment dataset. Extensive experimental results show that the proposed framework significantly outperforms state-of-the-art methods, and effectively learns and integrates different factors of the online article quality assessment.

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“…Hence, for public opinion government [ 2 , 3 ] and marketing [ 4 – 8 ], it is important to mine the law behind information diffusion on online media [ 5 ]. In the past decade, researchers begin to predict the future trends in information diffusion [ 9 – 19 ]. The prediction can be divided into two aspects such as user-level prediction and cascade-level prediction according to the granularity of predicted targets.…”

Section: Introductionmentioning

confidence: 99%

Modelling the Latent Semantics of Diffusion Sources in Information Cascade Prediction

Huang¹,

Zhou²,

Zhang³

et al. 2021

Computational Intelligence and Neuroscience

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Predicting the information spread tendency can help products recommendation and public opinion management. The existing information cascade prediction models are devoted to extract the chronological features from diffusion sequences but treat the diffusion sources as ordinary users. Diffusion source, the first user in the information cascade, can indicate the latent topic and diffusion pattern of an information item to mine user potential common interests, which facilitates information cascade prediction. In this paper, for modelling the abundant implicit semantics of diffusion sources in information cascade prediction, we propose a Diffusion Source latent Semantics-Fused cascade prediction framework, named DSSF. Specifically, we firstly apply diffusion sources embedding to model the special role of the source users. To learn the latent interaction between users and diffusion sources, we proposed a co-attention-based fusion gate which fuses the diffusion sources’ latent semantics with user embedding. To address the challenge that the distribution of diffusion sources is long-tailed, we develop an adversarial training framework to transfer the semantics knowledge from head to tail sources. Finally, we conduct experiments on real-world datasets, and the results show that modelling the diffusion sources can significantly improve the prediction performance. Besides, this improvement is limited for the cascades from tail sources, and the adversarial framework can help.

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Popularity Prediction on Online Articles with Deep Fusion of Temporal Process and Content Features

Cited by 68 publications

References 11 publications

Hot question prediction in Stack Overflow

Hot question prediction in Stack Overflow

Cognitive Representation Learning of Self-Media Online Article Quality

Modelling the Latent Semantics of Diffusion Sources in Information Cascade Prediction

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