News reprint analysis is gradually becoming a hot research topic. Most existing studies in news reprint analysis mainly focus on mining news reprint relations, there is little study exploring the detection of the patterns of news reprint yet. To fill this gap, we aim to identify news reprint patterns such as word variant, sentence conversion, and topic restatement, which can provide deep insights into the news propagation mechanism. The challenge of this question lies in how to dig domain information and obtain the deep semantic representation of news to discover the writing styles of various reprint patterns. This paper proposes a media domain information-driven multi-level semantic fusion (MDID-MLSF) approach. It simultaneously considers news media information and extracts word-sentence-paragraph-level news semantic information by the interactive matching mechanism. Specifically, Word Mover's Distance (WMD) algorithm and Multilayer Perceptron (MLP) are employed to obtain semantic information at the word level and sentence level. Then, the model utilizes attention-based hierarchical Bi-LSTM to obtain the paragraph level semantic information. Finally, news media information and different levels' semantic information are jointly modeled to detect the patterns of news reprint. We empirically evaluate the performance of the proposed model on a real-world dataset, the experimental results demonstrate the efficacy of the model.

This paper aims to predict user satisfaction for customer service chatbot in session level, which is of great practical significance yet rather untouched. It requires to explore the relationship between questions and answers across different rounds of interactions, and handle user bias. We propose an approach to model multi-round conversations within one session and take user information into account. Experimental results on a dataset from a real-world industrial customer service chatbot Alime demonstrate the good performance of our proposed model.

Online reviews are playing an increasingly important role in understanding and predicting users’ rating behavior, which brings great opportunities for users and organizations to make better decisio...

News media has become a prevalent information spreading platform, where news sites can reprint news from other sites. To better understand the mechanism of news propagation, it is necessary to model reprint behavior and predict whether a news site will reprint a piece of news. Most existing works in news reprint analysis focus on analyzing the semantic of news content, little work has been done on integrating reprint relationship among sites and news content for reprint prediction from the perspective of sites. The challenge of improving prediction performance lies in how to effectively incorporate these two kinds of information to learn a more comprehensive reprint behavior model. In this paper, we propose an Integrated Neural Reprint Prediction (INRP) model that considers both reprint relationship and news content. It models the reprint relationships as a directed weighted graph and maps it into a latent space to learn sites representations. During news content modeling process, sites representations are embedded as attention guidance to build up more site-specific content representations. Finally, sites and news representations are jointly modeled to predict whether a piece of news will be reprinted by a site. We empirically evaluate the performance of the proposed model on a real world dataset. Experimental results show that taking both the reprint relationship and news content information into consideration could allow us make more accurate analysis of reprint patterns. The mined patterns could serve as a feedback channel for both corporations and management departments.

JUUL has become a widely used brand of e-cigarettes which takes more than 70% of the market. Social media provides a popular platform for users to discuss the preference and perceptions of JUUL. The discussions are valuable for real-time monitoring of JUUL use. Current research on topic analysis of JUUL discussions mainly relies on human work, which takes much time and effort. This paper adopts a Semantics-assisted NMF topic analysis model to automatically discover topics from JUUL-related short posts on Reddit. By successfully merging the semantic relationships into traditional NMF, this model outperforms in discovering topics with keywords that are important but have a lower word frequency among the posts. Experimental results show the potential of this model in JUUL surveillance and control practice.

The opioid epidemic has become a serious public health crisis in the United States. Social media sources such as Reddit containing user-generated content may be a valuable safety surveillance platform to evaluate discussions discerning opioid use. This paper proposes a prior knowledge based neural attention model for opioid topics identification, which considers prior knowledge with attention mechanism. Experimental results on a real-world dataset show that our model can extract coherent topics, the identified less discussed but important topics provide more comprehensive information for opioid safety surveillance.