Since June 2013, when Brazil faced the largest and most significant mass protests in a generation, a political crisis is in course. In midst of this crisis, Brazilian politicians use social media to communicate with the electorate in order to retain or to grow their political capital. The problem is that many controversial topics are in course and deputies may prefer to avoid such themes in their messages. To characterize this behavior, we propose a method to accurately identify political and non-political tweets independently of the deputy who posted it and of the time it was posted. Moreover, we collected tweets of all congressmen who were active on Twitter and worked in the Brazilian parliament from October 2013 to October 2017. To evaluate our method, we used word clouds and a topic model to identify the main political and non-political latent topics in parliamentarian tweets. Both results indicate that our proposal is able to accurately distinguish political from non-political tweets. Moreover, our analyses revealed a striking fact: more than half of the messages posted by Brazilian deputies are non-political.
The task of identifying social media messages about political issues is not new in the literature. Paul proposed a semi-supervised approach to distinguish tweets related to politics from non-political ones. From a collection of news articles, the authors used a topic model approach to identify words related to political topics. performed a simple hashtag co-occurrence discovery procedure to identify political tweets. From a collection of tweets posted around the US election day, proposed a weakly supervised two-path bootstrapping approach for detecting and characterizing online hate speech. In the aforementioned studies @cite_14 @cite_10 @cite_2 , a initial list of hashtags or words was used as input for the creation of the final list of terms. Thus, these approaches are very difficult to be generalized or to be accurate over a long period of time. Nevertheless, the neural network architecture proposed by @cite_2 is similar to the one we propose in this article. However, we used a Convolutional Neural Network (CNN) with different filter size instead of a Long Short Term Memory network (LSTM), as the latter presented worse results in our experiments.
@cite_14 @cite_10 @cite_2
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Tables abbreviations of the main political parties, 1979-96 Part I. Rethinking Part systems Theory: 1. Introduction 2. Reexamining party systems theory in the third wave of democratisation Part II. The Brazilian Part Systems, Past and Present: 3. A legacy of party underdevelopment, 1822-1979 4. Elections, parties and society, 1979-96 5. Weak parties and autonomous politicians: party organisation in the catch-all parties 6. Patronage, clientelism, and patrimonialism Part III. Explaining Weak Party-System Institutionalisation: 7. Macrocomparative factors and post-1964 developments 8. Institutional rules and weak institutionalisation: incentives for legislators 9. Institutional rules and the party system: federalism, Malapportionment and presidentialism Part IV. The Party System, Economic Reform, and the Quality of Democracy: 10. Political institutions, state reform and economic stabilisation 11. Conclusion Notes Bibliography Index. In this study we investigate how social media shape the networked public sphere and facilitate communication between communities with different political orientations. We examine two networks of political communication on Twitter, comprised of more than 250,000 tweets from the six weeks leading up to the 2010 U.S. congressional midterm elections. Using a combination of network clustering algorithms and manually-annotated data we demonstrate that the network of political retweets exhibits a highly segregated partisan structure, with extremely limited connectivity between left- and right-leaning users. Surprisingly this is not the case for the user-to-user mention network, which is dominated by a single politically heterogeneous cluster of users in which ideologically-opposed individuals interact at a much higher rate compared to the network of retweets. To explain the distinct topologies of the retweet and mention networks we conjecture that politically motivated individuals provoke interaction by injecting partisan content into information streams whose primary audience consists of ideologically-opposed users. We conclude with statistical evidence in support of this hypothesis. In the wake of a polarizing election, social media is laden with hateful content. To address various limitations of supervised hate speech classification methods including corpus bias and huge cost of annotation, we propose a weakly supervised two-path bootstrapping approach for an online hate speech detection model leveraging large-scale unlabeled data. This system significantly outperforms hate speech detection systems that are trained in a supervised manner using manually annotated data. Applying this model on a large quantity of tweets collected before, after, and on election day reveals motivations and patterns of inflammatory language.