# Combining Post Sentiments and User Participation for Extracting Public Stances from Twitter
## 1 Introduction
For example, methods have been developed for spam detection [6], detection of spam accounts or spammers [7], or even spammer groups [8] or link farming [9]. Using features from user profiles, social relation graph, and propagation path, there are many ongoing research efforts to distinguish between normal users and bots. Since online identities cannot be reliably verified, we do not intend to detect the stance that each single person holds on the target accurately. In contrast, the goal of this paper is to extract the \"public stance\", which is the aggregate stance of the general public on a given target from online posts in social networks. By summarizing post sentiments for opinion holders on the target and user participation in discussions on related topics, we expect to get the general idea about what the public thinks about the target. In this paper, we investigate the effectiveness of deep learning methods for sentiment classification of short texts and the impact of combining dynamic user participation in influence analysis for extracting public stances on popular topics. First, sentiment classification is done by long short-term memory (LSTM) [10], which learns the long-term dependency between words in short texts, with the representation of words as vectors by word embedding models such as Word2 Vec. Second, for user influence estimation, different types of user actions on tweets, such as posting, sharing, and retweeting are modeled as a user-post interaction graph. Then, we modify TURank [11] by assigning different edge weightings for different actions on the graph for link analysis. Finally, we estimate the public stance on a specific topic or a set of comparable topics by the ratio of positive, negative, and neutral scores, which are calculated by a weighted sum of content sentiments by the user structural influence on the topic. In our experiments, we compare the performance of LSTM with Naive Bayes (NB), extreme learning machine (ELM) [12], and convolutional neural networks (CNNs) for sentiment classification of several social datasets. Then, we apply the proposed method to extract public stances about the 2016 U.S. Presidential Election. As the experimental results show, for sentiment classification of tweets, LSTM can perform better than conventional probabilistic model and neural networks with the best F-measure of 72.97%. For stance extraction, our proposed method can achieve better performance than different sentiment measures as in Bermingham and Smeaton [13], including the unweighted _Sent_, _SoV_, _SoV\({}_{p}\)_, _SoV\({}_{n}\)_, with a mean absolute error (MAE) of 5.34%. With the inclusion of content weighting by user authority, we can further obtain the best performance with a MAE of 0.68%. This shows the potential of using deep learning methods for sentiment analysis and combining user participation for extracting public stances from social media. Further investigation is needed to verify the effectiveness of the proposed approach on different social media in larger scale. The major contribution of this paper can be summarized as follows:  
1. We focus on extracting the aggregate stance of the general public on a single topic and a set of comparable topics, where no datasets are available. This task is different from existing research on stance detection where stance labels are available in open datasets. 2. We modify existing methods of link analysis such as TURank to consider the different edge weighting for posting, sharing, and retweeting actions. 3. We design a feasible method to calculate user influence scores and combine tweets content sentiments for extracting public stances on popular topics such as election. The remainder of this paper is organized as follows: Section 2 lists the related works, and Section 3 describes the proposed method. The experimental results and discussions are described in Sections 4 and 5, respectively. Finally, Section 6 presents our conclusions.