Data Portraits and Intermediary Topics: Encouraging Exploration of Politically Diverse Profiles

In micro-blogging platforms, people connect and interact with others. However, due to cognitive biases, they tend to interact with like-minded people and read agreeable information only. Many efforts to make people connect with those who think differently have not worked well. In this paper, we hypothesize, first, that previous approaches have not worked because they have been direct -- they have tried to explicitly connect people with those having opposing views on sensitive issues. Second, that neither recommendation or presentation of information by themselves are enough to encourage behavioral change. We propose a platform that mixes a recommender algorithm and a visualization-based user interface to explore recommendations. It recommends politically diverse profiles in terms of distance of latent topics, and displays those recommendations in a visual representation of each user's personal content. We performed an "in the wild" evaluation of this platform, and found that people explored more recommendations when using a biased algorithm instead of ours. In line with our hypothesis, we also found that the mixture of our recommender algorithm and our user interface, allowed politically interested users to exhibit an unbiased exploration of the recommended profiles. Finally, our results contribute insights in two aspects: first, which individual differences are important when designing platforms aimed at behavioral change; and second, which algorithms and user interfaces should be mixed to help users avoid cognitive mechanisms that lead to biased behavior.Comment: 12 pages, 7 figures. To be presented at ACM Intelligent User Interfaces 201

What’s Happening Around the World? A Survey and Framework on Event Detection Techniques on Twitter

© 2019, Springer Nature B.V. In the last few years, Twitter has become a popular platform for sharing opinions, experiences, news, and views in real-time. Twitter presents an interesting opportunity for detecting events happening around the world. The content (tweets) published on Twitter are short and pose diverse challenges for detecting and interpreting event-related information. This article provides insights into ongoing research and helps in understanding recent research trends and techniques used for event detection using Twitter data. We classify techniques and methodologies according to event types, orientation of content, event detection tasks, their evaluation, and common practices. We highlight the limitations of existing techniques and accordingly propose solutions to address the shortcomings. We propose a framework called EDoT based on the research trends, common practices, and techniques used for detecting events on Twitter. EDoT can serve as a guideline for developing event detection methods, especially for researchers who are new in this area. We also describe and compare data collection techniques, the effectiveness and shortcomings of various Twitter and non-Twitter-based features, and discuss various evaluation measures and benchmarking methodologies. Finally, we discuss the trends, limitations, and future directions for detecting events on Twitter

PFU: Profiling Forum users in online social networks, a knowledge driven data mining approach

Online Social Networks (OSNs) provide platform to raise opinions on various issues, create and spread news rapidly in Online Social Network Forums (OSNFs). This work proposes a novel method for Profiling Forum Users (PFU) by exploring their behavioral characteristics based on their involvement in various topics of discussion and number of posts in respective topics posted by them in OSNFs dynamically. Modeling the proposed method mathematically, the PFU algorithm is illustrated for its adequacy and accuracy

BlogForever D2.6: Data Extraction Methodology

This report outlines an inquiry into the area of web data extraction, conducted within the context of blog preservation. The report reviews theoretical advances and practical developments for implementing data extraction. The inquiry is extended through an experiment that demonstrates the effectiveness and feasibility of implementing some of the suggested approaches. More specifically, the report discusses an approach based on unsupervised machine learning that employs the RSS feeds and HTML representations of blogs. It outlines the possibilities of extracting semantics available in blogs and demonstrates the benefits of exploiting available standards such as microformats and microdata. The report proceeds to propose a methodology for extracting and processing blog data to further inform the design and development of the BlogForever platform

Novel platform for topic group mining, crowd opinion analysis and opinion leader identification in on-line social network platforms

In recent years, topic group mining and massive crowd opinion analysis from on-line social network platforms have become some of the most important tasks not only in research area but also in industry. Systems of this sort can identify similar topics from a very large dataset, group them together based on the topic, and analyse the inclination of the content's owner. To solve this problem, which involves research from a number of different areas, an integrated platform needs to be proposed. Most community mining techniques treat the network as a graph where nodes represent users and edges reflect user relationship between two users. One obvious drawback of these approaches is that it can only utilise the explicit user relationships provided by on-line social network platforms. All other possible relationships will be ignored. Some on-line social network platforms restrict the length of content a user can publish. This causes traditional document clustering methods to perform poorly. Meanwhile, the restriction of content length also affects opinion mining performance since most content lacks contextual features. Hence, other context features that are not immediately or obviously related need to be investigated to improve performance in user inclination classification. This research proposes a novel three layered platform. Two core technologies of the platform are topic group mining and user inclination analysis. The integrated approach was evaluated by a series of experiments to examine each core technology. The results indicate that the proposed integrated platform is able to produce the following results. 1) Scores up to 0.82 by V-measure evaluation function in topic group mining. 2) High accuracy rate in inclination mining. 3) A flexible and adaptable platform design which can accommodate different on-line social networks easily

Automatic extraction of mobility activities in microblogs

Tese de Mestrado Integrado. Engenharia Informática e Computação. Faculdade de Engenharia. Universidade do Porto. 201