# Combining Post Sentiments and User Participation for Extracting Public Stances from Twitter
## 1 Introduction  
Public opinions are important in a society where policy makers need to collect data, especially when there are major issues for the general public. Conventionally, public opinions are usually gathered by questionnaires, which is a tedious and time-consuming task. With the popularity of social networking platforms, it's becoming more convenient for people to express their opinionsonline, and the availability of social Application Programming Interfaces (APIs) facilitates collecting opinions more easily. To better understand what people think about a topic, there are generally two approaches to sentiment analysis: content-based and structure-based. For content-based analysis, sentiment classification techniques have been extensively investigated and compared such as the bag-of-words model using Term Frequency-Inverse Document Frequency (TF-IDF), the probabilistic model using Naive Bayes and the blackbox model using neural networks. However, these methods usually need hand-crafted features. For social media which are very short and diverse in topic, it's more difficult to obtain useful features for classification and without suitable features, it's hard to achieve good classification results. Thus, a more effective method for short text sentiment classification is needed. To obtain more useful features for short texts in social media, we utilize the idea of distributed representation of documents where each input is represented by many features and each feature is involved in many possible inputs. Specifically, for social media texts, word embedding models such as Word2 Vec [1] and GloVe [2] are often used for distributed representation. In addition to text contents, public opinions can also be affected by social relations and societal status. People tend to follow what famous people say or do. Thus, user authority is one of the important factors in public opinion analysis. There are two major ways to judge user authority. One is the use of static social relations such as friends, fans, or followers in different social networking platforms. These are explicit relationships that can be evident from the user profile in the platform. Link analysis methods such as HITS algorithm [3] or PageRank [4] are often used to analyze such relationships. The other is the dynamic structures formed from user participation in social media. For example, users can like, retweet, and forward messages to their acquaintances. These are implicit relationships that can only be learned from the history of user activities. Information diffusion models [5] investigate how information is propagated through social networks. Influential actors might play key roles since they tend to forward information to the largest extent by their number of acquaintances and the degree of penetration. To exploit these social relations, online identity verification is a challenge. Social networking platforms are considered publicly shared assets for the general public to freely express their ideas and opinions. For users in social networks such as Twitter, it's not required to provide authentic information in user profiles. The real identity of users cannot be reliably verified. This can cause problems if the user accounts were faked by spammers or fraud groups who deploy bots or programs that could be easily reproduced in a large scale for advertisements, spreading inappropriate speech (sex, violence, hate, to name a few), or impersonating people for fun or fraudulent intent. To deal with such a problem of \"source credibility\", there are lots of efforts. On the one hand, major social networking platforms enforce different rules for protecting the public venues from inappropriate behaviors and languages. When users violate these rules, their messages will be deleted and their accounts will be suspended. On the other hand, there are technical methods to detect such malicious behaviors in different ways.