Extracting emotional support in Online Health Communities provides insightful information about patients’ emotional states. Current computational approaches to identifying emotional messages, i.e., messages that contain emotional support, are typically based on a set of handcrafted features. In this paper, we show that high-level and abstract features derived from a combination of convolutional neural networks (CNN) with Long Short Term Memory (LSTM) networks can be successfully employed for emotional message identification and can obviate the need for handcrafted features.

One important problem in task-based conversations is that of effectively updating the belief estimates of user-mentioned slot-value pairs.Given a user utterance, the intent of a slot-value pair is captured using dialog acts (DA) expressed in that utterance.However, in certain cases, DA's fail to capture the actual update intent of the user.In this paper, we describe such cases and propose a new type of semantic class for user intents.This new type, Update Intents (UI), is directly related to the type of update a user intends to perform for a slot-value pair.We define five types of UI's, which are independent of the domain of the conversation.We build a multi-class classification model using LSTM's to identify the type of UI in user utterances in the Restaurant and Shopping domains.Experimental results show that our models achieve strong classification performance in terms of F-1 score.

Clickbaits are articles with misleading titles, exaggerating the content on the landing page. Their goal is to entice users to click on the title in order to monetize the landing page. The content on the landing page is usually of low quality. Their presence in user homepage stream of news aggregator sites (e.g., Yahoo news, Google news) may adversely impact user experience. Hence, it is important to identify and demote or block them on homepages. In this paper, we present a machine-learning model to detect clickbaits. We use a variety of features and show that the degree of informality of a webpage (as measured by different metrics) is a strong indicator of it being a clickbait. We conduct extensive experiments to evaluate our approach and analyze properties of clickbait and non-clickbait articles. Our model achieves high performance (74.9% F-1 score) in predicting clickbaits.

Online discussion forums have become a popular medium for users to discuss with and seek information from other users having similar interests. A typical discussion thread consists of a sequence of posts posted by multiple users. Each post in a thread serves a different purpose providing different types of information and, thus, may not be equally useful for all applications. Identifying the purpose and nature of each post in a discussion thread is thus an interesting research problem as it can help in improving information extraction and intelligent assistance techniques. We study the problem of classifying a given post as per its purpose in the discussion thread and employ features based on the post's content, structure of the thread, behavior of the participating users, and sentiment analysis of the post's content. We evaluate our approach on two forum data sets belonging to different genres and achieve strong classification performance. We also analyze the relative importance of different features used for the post classification task. Next, as a use case, we describe how the post class information can help in thread retrieval by incorporating this information in a state‐of‐the‐art thread retrieval model.

A typical discussion thread in an online fo-rum spans multiple pages involving par-ticipation from multiple users and thus, may contain multiple view-points and so-lutions. A user interested in the topic of discussion or having a problem similar to being discussed in the thread may not want to read all the previous posts but only a few selected posts that provide her a concise summary of the ongoing discussion. This paper describes an extractive summariza-tion technique that uses textual features and dialog act information of individual messages to select a subset of posts. Pro-posed approach is evaluated using two real life forum datasets. 1

Sentiment analysis has been widely researched in the domain of online review sites with the aim of getting summarized opinions of product users about different aspects of the products. However, there has been little work focusing on identifying the polarity of sentiments expressed by users in online health communities such as cancer support forums, etc. Online health communities act as a medium through which people share their health concerns with fellow members of the community and get social support. Identifying sentiments expressed by members in a health community can be helpful in understanding dynamics of the community such as dominant health issues, emotional impacts of interactions on members, etc. In this work, we perform sentiment classification of user posts in an online cancer support community (Cancer Survivors Network). We use Domain-dependent and Domain-independent sentiment features as the two complementary views of a post and use them for post classification in a semi-supervised setting using the co-training algorithm. Experimental results demonstrate effectiveness of our methods.

Online forums have become a popular source of information due to the unique nature of information they contain. Internet users use these forums to get opinions of other people on issues and to find factual answers to specific questions. Topics discussed in online forum threads can be subjective seeking personal opinions or non-subjective seeking factual information. Hence, knowing subjectivity orientation of threads would help forum search engines to satisfy user's information needs more effectively by matching the subjectivities of user's query and topics discussed in the threads in addition to lexical match between the two. We study methods to analyze the subjectivity of online forum threads. Experimental results on a popular online forum demonstrate the effectiveness of our methods.