Polarization is a troubling phenomenon that can lead to societ al divisions and hurt the democratic process. It is therefore important to develop methods to reduce it. We propose an algorithmic solution to the problem of reducing polarization. The core idea is to expose users to content that challenges their point of view, with the hope broadening their perspective, and thus reduce their polarity. Our method takes into account several aspects of the problem, such as the estimated polarity of the user, the probability of accepting the recommendation, the polarity of the content, and popularity of the content being recommended. We evaluate our recommendations via a large-scale user study on Twitter users that were actively involved in the discussion of the US elections results. Results shows that, in most cases, the factors taken into account in the recommendation affect the users as expected, and thus capture the essential features of the problem.
using ideas from in order to make use of content from high degree (authoritative users) in the ranking and show the ranking. using ideas from we diversify the topics. using ideas from @cite_3 , we include an acceptance probability into our recommendation.
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We propose a new unsupervised learning technique for extracting information from large text collections. We model documents as if they were generated by a two-stage stochastic process. Each author is represented by a probability distribution over topics, and each topic is represented as a probability distribution over words for that topic. The words in a multi-author paper are assumed to be the result of a mixture of each authors' topic mixture. The topic-word and author-topic distributions are learned from data in an unsupervised manner using a Markov chain Monte Carlo algorithm. We apply the methodology to a large corpus of 160,000 abstracts and 85,000 authors from the well-known CiteSeer digital library, and learn a model with 300 topics. We discuss in detail the interpretation of the results discovered by the system including specific topic and author models, ranking of authors by topic and topics by author, significant trends in the computer science literature between 1990 and 2002, parsing of abstracts by topics and authors and detection of unusual papers by specific authors. An online query interface to the model is also discussed that allows interactive exploration of author-topic models for corpora such as CiteSeer. We propose a novel approach based on Formal Concept Analysis for Topic Detection.Our proposal overcomes traditional problems of the clustering and classification techniques.We analyse the parameters involved in the process in a Twitter-based framework.We propose a topic selection methodology based on the stability concept.We overcome the state-of-the-art results for the task. The Topic Detection Task in Twitter represents an indispensable step in the analysis of text corpora and their later application in Online Reputation Management. Classification, clustering and probabilistic techniques have been traditionally applied, but they have some well-known drawbacks such as the need to fix the number of topics to be detected or the problem of how to integrate the prior knowledge of topics with the detection of new ones. This motivates the current work, where we present a novel approach based on Formal Concept Analysis (FCA), a fully unsupervised methodology to group similar content together in thematically-based topics (i.e., the FCA formal concepts) and to organize them in the form of a concept lattice. Formal concepts are conceptual representations based on the relationships between tweet terms and the tweets that have given rise to them. It allows, in contrast to other approaches in the literature, their clear interpretability. In addition, the concept lattice represents a formalism that describes the data, explores correlations, similarities, anomalies and inconsistencies better than other representations such as clustering models or graph-based representations. Our rationale is that these theoretical advantages may improve the Topic Detection process, making them able to tackle the problems related to the task. To prove this point, our FCA-based proposal is evaluated in the context of a real-life Topic Detection task provided by the Replab 2013 CLEF Campaign. To demonstrate the efficiency of the proposal, we have carried out several experiments focused on testing: (a) the impact of terminology selection as an input to our algorithm, (b) the impact of concept selection as the outcome of our algorithm, and; (c) the efficiency of the proposal to detect new and previously unseen topics (i.e., topic adaptation). An extensive analysis of the results has been carried out, proving the suitability of our proposal to integrate previous knowledge of prior topics without losing the ability to detect novel and unseen topics as well as improving the best Replab 2013 results. In this work we present a novel item recommendation approach that aims at improving Collaborative Filtering (CF) in social tagging systems using the information about tags and time. Our algorithm follows a two-step approach, where in the first step a potentially interesting candidate item-set is found using user-based CF and in the second step this candidate item-set is ranked using item-based CF. Within this ranking step we integrate the information of tag usage and time using the Base-Level Learning (BLL) equation coming from human memory theory that is used to determine the reuse-probability of words and tags using a power-law forgetting function. As the results of our extensive evaluation conducted on data-sets gathered from three social tagging systems (BibSonomy, CiteULike and MovieLens) show, the usage of tag-based and time information via the BLL equation also helps to improve the ranking and recommendation process of items and thus, can be used to realize an effective item recommender that outperforms two alternative algorithms which also exploit time and tag-based information. We propose an unsupervised learning technique for extracting information about authors and topics from large text collections. We model documents as if they were generated by a two-stage stochastic process. An author is represented by a probability distribution over topics, and each topic is represented as a probability distribution over words. The probability distribution over topics in a multi-author paper is a mixture of the distributions associated with the authors. The topic-word and author-topic distributions are learned from data in an unsupervised manner using a Markov chain Monte Carlo algorithm. We apply the methodology to three large text corpora: 150,000 abstracts from the CiteSeer digital library, 1740 papers from the Neural Information Processing Systems (NIPS) Conferences, and 121,000 emails from the Enron corporation. We discuss in detail the interpretation of the results discovered by the system including specific topic and author models, ranking of authors by topic and topics by author, parsing of abstracts by topics and authors, and detection of unusual papers by specific authors. Experiments based on perplexity scores for test documents and precision-recall for document retrieval are used to illustrate systematic differences between the proposed author-topic model and a number of alternatives. Extensions to the model, allowing for example, generalizations of the notion of an author, are also briefly discussed.