A Supervised Approach to Predict the Hierarchical Structure of Conversation Threads for Comments

Abstract

User-generated texts such as comments in social media are rich sources of information. In general, the reply structure of comments is not publicly accessible on the web. Websites present comments as a list in chronological order. This way, some information is lost. A solution for this problem is to reconstruct the thread structure (RTS) automatically. RTS predicts a semantic tree for the reply structure, useful for understanding users' behaviours and facilitating follow of the actual conversation streams. This paper works on RTS task in blogs, online news agencies, and news websites. These types of websites cover various types of articles reflecting the real-world events. People with different views participate in arguments by writing comments. Comments express opinions, sentiments, or ideas about articles. The reply structure of threads in these types of websites is basically different from threads in the forums, chats, and emails. To perform RTS, we define a set of textual and nontextual features. Then, we use supervised learning to combine these features. The proposed method is evaluated on five different datasets. The accuracy of the proposed method is compared with baselines. The results reveal higher accuracy for our method in comparison with baselines in all datasets.

Hierarchical Dirichlet Gaussian Marked Hawkes Process for Narrative Reconstruction in Continuous Time Domain

“…(Wang et al, 2011a) uses Conditional Random Field to reconstruct reply structure in discussion corpus. (Balali et al, 2014) uses content, time and author information as features of a single post with rank SVM to reconstruct thread structure. (Dehghani et al, 2013;Aumayr et al, 2011) uses SVM and a decision tree with meta information of posts.…”

A Supervised Method to Predict the Popularity of News Articles

“…The framework focusing on features from two sources of social interactions inherent in online discussions: the comment tree and the user graph. Even, the reply tree of comments includes some valuable information such as showing important comments [13]. Also, the user's comments are used in different applications like user behavior analysis, finding important users [14] and post summarization [15].…”

“…Some websites show replied comments as a tree structure and some as a list sorted in chronological (or reverse) order. Some papers work on automatic reconstruction of the tree structure from a list [13,19,20].…”

The Collaborative Search by Tag-Based User Profile in Social Media

“…Xu et al [ 20 ] measured the semantic relatedness between Flickr images from the tag-based perspectives. Balali et al [ 21 ] presented a supervised approach to predicting and reorganizing the hierarchical structure of conversation threads for user-generated text in social media. The tag-based profiles were further studied and investigated to facilitate personalized search [ 22 – 24 ].…”