Detecting opinion polarisation on twitter by constructing pseudo-bimodal networks of mentions and retweets

© Springer International Publishing Switzerland 2016.We present a novel approach to analyze and visualize opinion polarisation on Twitter based on graph features of communication networks extracted from tweets. We show that opinion polarisation can be legibly observed on unimodal projections of artificially created bimodal networks, where the most popular users in retweet and mention networks are considered nodes of the second mode. For this purpose, we select a subset of top users based on their Page Rank values and assign them to be the second mode in our networks, thus called pseudo-bimodal. After projecting them onto the set of “bottom” users and vice versa, we get unimodal networks with more distinct clusters and visually coherent community separation. We developed our approach on a dataset gathered during the Russian protest meetings on 24th of December, 2011 and tested it on another dataset by Conover [13] used to analyze political polarisation, showing that our approach not only works well on our data but also improves the results from previous research on that phenomena

Extended Call-by-Push-Value: Reasoning About Effectful Programs and Evaluation Order

Traditionally, reasoning about programs under varying evaluation regimes (call-by-value, call-by-name etc.) was done at the meta-level, treating them as term rewriting systems. Levy’s call-by-push-value (CBPV) calculus provides a more powerful approach for reasoning, by treating CBPV terms as a common intermediate language which captures both call-by-value and call-by-name, and by allowing equational reasoning about changes to evaluation order between or within programs. We extend CBPV to additionally deal with call-by-need, which is non-trivial because of shared reductions. This allows the equational reasoning to also support call-by-need. As an example, we then prove that call-by-need and call-by-name are equivalent if nontermination is the only side-effect in the source language. We then show how to incorporate an effect system. This enables us to exploit static knowledge of the potential effects of a given expression to augment equational reasoning; thus a program fragment might be invariant under change of evaluation regime only because of knowledge of its effects

Pseudo-bimodal community detection in Twitter-based networks

© 2016 IEEE.We present a novel approach to clustering Twitter users and characterizing their preferences (political or otherwise) based on the features of communication networks extracted from their tweets. We make the assumption that central users in the network, the so-called 'top', or 'power' users, set the agenda, while other, 'regular' users often retweet and/or mention their tweets, and behavior towards 'top' users differs from the behaviour of 'regular' users towards each other. We show that network clustering on Twitter can be observed more distinctively on unimodal projections of specially created bimodal networks (bipartite graphs), where top users in the networks are artificially separated into a second part according to node centrality measures. We evaluate our approach on Twitter-based datasets of mentions and retweets related to Russian political protests and a benchmark English-language Twitter dataset with distinctly polarized clusters; we compare various centrality measures and show that our algorithm yields high modularity in the resulting community structure

Detecting opinion polarisation on twitter by constructing pseudo-bimodal networks of mentions and retweets

© Springer International Publishing Switzerland 2016.We present a novel approach to analyze and visualize opinion polarisation on Twitter based on graph features of communication networks extracted from tweets. We show that opinion polarisation can be legibly observed on unimodal projections of artificially created bimodal networks, where the most popular users in retweet and mention networks are considered nodes of the second mode. For this purpose, we select a subset of top users based on their Page Rank values and assign them to be the second mode in our networks, thus called pseudo-bimodal. After projecting them onto the set of “bottom” users and vice versa, we get unimodal networks with more distinct clusters and visually coherent community separation. We developed our approach on a dataset gathered during the Russian protest meetings on 24th of December, 2011 and tested it on another dataset by Conover [13] used to analyze political polarisation, showing that our approach not only works well on our data but also improves the results from previous research on that phenomena

Detecting opinion polarisation on twitter by constructing pseudo-bimodal networks of mentions and retweets

© Springer International Publishing Switzerland 2016.We present a novel approach to analyze and visualize opinion polarisation on Twitter based on graph features of communication networks extracted from tweets. We show that opinion polarisation can be legibly observed on unimodal projections of artificially created bimodal networks, where the most popular users in retweet and mention networks are considered nodes of the second mode. For this purpose, we select a subset of top users based on their Page Rank values and assign them to be the second mode in our networks, thus called pseudo-bimodal. After projecting them onto the set of “bottom” users and vice versa, we get unimodal networks with more distinct clusters and visually coherent community separation. We developed our approach on a dataset gathered during the Russian protest meetings on 24th of December, 2011 and tested it on another dataset by Conover [13] used to analyze political polarisation, showing that our approach not only works well on our data but also improves the results from previous research on that phenomena

Le Petit Troyen : journal démocratique régional ["puis" journal quotidien de la démocratie de l'Est "puis" grand quotidien de la Champagne]

29 décembre 19161916/12/29 (A36,N13291).Appartient à l’ensemble documentaire : ChArdenn

Detecting opinion polarisation on twitter by constructing pseudo-bimodal networks of mentions and retweets

© Springer International Publishing Switzerland 2016.We present a novel approach to analyze and visualize opinion polarisation on Twitter based on graph features of communication networks extracted from tweets. We show that opinion polarisation can be legibly observed on unimodal projections of artificially created bimodal networks, where the most popular users in retweet and mention networks are considered nodes of the second mode. For this purpose, we select a subset of top users based on their Page Rank values and assign them to be the second mode in our networks, thus called pseudo-bimodal. After projecting them onto the set of “bottom” users and vice versa, we get unimodal networks with more distinct clusters and visually coherent community separation. We developed our approach on a dataset gathered during the Russian protest meetings on 24th of December, 2011 and tested it on another dataset by Conover [13] used to analyze political polarisation, showing that our approach not only works well on our data but also improves the results from previous research on that phenomena