SENTI2POP: SentimentAware Topic Popularity Prediction on Social Media Topic popularity prediction is an important task on social media, which aims at predicting the ongoing trends of topics according to logged historical textbased records. However, only limited existing approaches apply sentiment analysis to facilitate popularity prediction. Public sentiment is worth taking into consideration because the topics with strong sentiment tend to spread faster and broader on social media. In this paper, we propose a novel framework, SENTI2POP, to predict topic popularity utilizing sentiment information. We first adapt a stateofart popularity quantification method to capture the topic popularity, and then design a novel treelike network (TreeNet) combining Long ShortTerm Memory (LSTM) and Convolutional Neural Network (CNN) for sentiment analysis. In addition, we propose a sentimentaware time series prediction approach based on Dynamic Time Warping (DTW) and Autoregressive Integrated Moving Average model (ARIMA) to predict topic popularity. We prove by experiments that SENTI2POP outperforms the existing popularity prediction models on a realworld Twitter dataset by reducing the prediction error. Experimental results also show that SENTI2POP could be applied to improve the accuracy of most nonsentiment popularity prediction models.

Conversational Structure Aware and Context Sensitive Topic Model for Online Discussions. Millions of online discussions are generated everyday on social media platforms. Topic modelling is an efficient way of better understanding large text datasets at scale. Conventional topic models have had limited success in online discussions, and to overcome their limitations, we use the discussion thread tree structure and propose a popularity metric to quantify the number of replies to a comment to extend the frequency of word occurrences, and the transitivity concept to characterize topic dependency among nodes in a nested discussion thread. We build a Conversational Structure Aware Topic Model (CSATM) based on popularity and transitivity to infer topics and their assignments to comments. Experiments on real forum datasets are used to demonstrate improved performance for topic extraction with six different measurements of coherence and impressive accuracy for topic assignments.