The emergence of synthetic data represents a pivotal shift in modern machine learning, offering a solution to satisfy the need for large volumes of data in domains where real data is scarce, highly private, or difficult to obtain. We investigate the feasibility of creating realistic, large-scale synthetic datasets of user-generated content, noting that such content is increasingly prevalent and a source of frequently sought information. Large language models (LLMs) offer a starting point for generating synthetic social media discussion threads, due to their ability to produce diverse responses that typify online interactions. However, as we demonstrate, straightforward application of LLMs yields limited success in capturing the complex structure of online discussions, and standard prompting mechanisms lack sufficient control. We therefore propose a multi-step generation process, predicated on the idea of creating compact representations of discussion threads, referred to as scaffolds. Our framework is generic yet adaptable to the unique characteristics of specific social media platforms. We demonstrate its feasibility using data from two distinct online discussion platforms. To address the fundamental challenge of ensuring the representativeness and realism of synthetic data, we propose a portfolio of evaluation measures to compare various instantiations of our framework.

Artificial intelligence has led to some pivotal changes in the financial sector. Fintech companies are projected to spend over $12 billion on AI this year. A growing number of traders are taking advantage of AI technology to make more informed trading decisions. AI technology has actually changed stock market investing as we know it. There are a number of ways that traders can benefit from AI.

Since its introduction in 2011, there have been over 4000 MOOCs on various subjects on the Web, serving over 35 million learners. MOOCs have shown the ability to democratize knowledge dissemination and bring the best education in the world to every learner. However, the disparate distances between participants, the size of the learner population, and the heterogeneity of the learners' backgrounds make it extremely difficult for instructors to interact with the learners in a timely manner, which adversely affects learning experience. To address the challenges, in this thesis, we propose a framework: educational content linking. By linking and organizing pieces of learning content scattered in various course materials into an easily accessible structure, we hypothesize that this framework can provide learners guidance and improve content navigation. Since most instruction and knowledge acquisition in MOOCs takes place when learners are surveying course materials, better content navigation may help learners find supporting information to resolve their confusion and thus improve learning outcome and experience. To support our conjecture, we present end-to-end studies to investigate our framework around two research questions: 1) can manually generated linking improve learning? 2) can learning content be generated with machine learning methods? For studying the first question, we built an interface that present learning materials and visualize the linking among them simultaneously. We found the interface enables users to search for desired course materials more efficiently, and retain more concepts more readily. For the second question, we propose an automatic content linking algorithm based on conditional random fields. We demonstrate that automatically generated linking can still lead to better learning, although the magnitude of the improvement over the unlinked interface is smaller.

Recently, there has been a sibilation about Chatbots. Chatbots are the applications that, instead of a graphical interface, make use of conversational UI. With any buzz-commendable innovation, there is the typical perplexity about what precisely a Chatbot is – and relying upon what number of articles you have perused, it is anywhere betwixt an improved IVR or the innovation which will bring about world peace. Conversational UI & Chatbots - neither of the two are new. Because of the underneath mentionedreasons the prevailing sibilation is ascribed. All organizations must think whether they need a system to have a "Conversational Application" – similarly they are needed for mobile applications.

Online discussions are growing as a popular, effective and reliable source of information for users because of their liveliness, flexibility and up-to-date information. Online discussions are usually developed and advanced by groups of users with various backgrounds and intents. However because of their diversities in topics and issues discussed by the users, supervised methods are not able to accurately model such dynamic conditions. In this paper, we propose a novel unsupervised generative model to derive aspect-action pairs from online discussions. The proposed method simultaneously captures and models these two features with their relationships that exist in each thread. We assume that each user post is generated by a mixture of aspect and action topics. Therefore, we design a model that captures the latent factors that incorporates the aspect types and intended actions, which describe how users develop a topic in a discussion. In order to demonstrate the effectiveness of our approach, we empirically compare our model against the state of the art methods on large-scale discussion dataset, crawled from apple discussions with over 3.3 million user posts from 340k discussion threads.

Self-publication of news on Web sites is becoming a common application platform to enable more engaging interaction among users. Discussion in the form of comments following news postings can be effectively facilitated if the service provider can recommend articles based on not only the original news itself but also the thread of changing comments. This turns the traditional news recommendation to a "discussion moderator" that can intelligently assist online forums. In this work, we present a framework to implement such adaptive news recommendation. In addition, to alleviate the problem of recommending essentially identical articles, the relationship (duplication, generalization or specialization) between suggested news articles and the original posting is investigated. Experiments indicate that our proposed solutions provide an enhanced news recommendation service in forum-based social media.

The goal of the Pedagogical Discourse project is to develop instructional tools that will help students and instructors use discussion boards more effectively, with an emphasis on automatically assessing discussion activities and building tools for promoting student discussion participation and learning. In this paper, we present a two related participation and learning scaffolding tools that exploit natural language processing and information retrieval techniques. The PedaBot tool is designed to aid student knowledge acquisition and promote reflection about course topics by connecting related discussions from a knowledge base of past discussions to the current discussion thread. The MentorMatch tool aims at promoting student participation using student mentors, i.e., course peers with a relatively good understanding of a particular topic. The system identifies students who often provide answers on a given topic and encourages classmates to invite mentors to participate in related discussions. Both tools have been integrated into a live discussion board that is used by an undergraduate computer science course. This paper describes our approaches to applying information retrieval and natural language processing techniques in the development of the tools and presents initial results from instrumentation and survey.