scholarly journals Learning Political Polarization on Social Media Using Neural Networks

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 47177-47187 ◽  
Author(s):  
Loris Belcastro ◽  
Riccardo Cantini ◽  
Fabrizio Marozzo ◽  
Domenico Talia ◽  
Paolo Trunfio
Keyword(s):  
Neural Networks ◽  
Social Media ◽  
Political Polarization

Political Interaction Beyond Party Lines: Communication Ties and Party Polarization in Parliamentary Twitter Networks

2021 ◽  
pp. 089443932098756
Author(s):  
Marc Esteve Del Valle ◽  
Marcel Broersma ◽  
Arnout Ponsioen
Keyword(s):  
Social Media ◽  
Political Parties ◽  
Geographical Area ◽  
Political Polarization ◽  
Exponential Random Graph Models ◽  
Party Polarization ◽  
Exponential Random Graph ◽  
Political Networks ◽  
Use Of Social Media ◽  
High Degree

A growing body of research has examined the uptake of social media by politicians, the formation of communication ties in online political networks, and the interplay between social media and political polarization. However, few studies have analyzed how social media are affecting communication in parliamentary networks. This is especially relevant in highly fragmented political systems in which collaboration between political parties is crucial to win support in parliament. Does MPs’ use of social media foster communications among parliamentarians who think differently, or does it result in like-minded clusters polarized along party lines, confining MPs to those who think alike? This study analyzes the formation of communication ties and the degree of homophily in the Dutch MPs’ @mention Twitter network. We employed exponential random graph models on a 1-year sample of all tweets in which Dutch MPs mentioned each other ( N = 7,356) to discover the network parameters (reciprocity, popularity, and brokerage) and individual attributes (seniority, participation in the parliamentary commissions, age, gender, and geographical area) that facilitate communication ties among parliamentarians. Also, we measured party polarization by calculating the external–internal index of the mentions. Dutch MPs’ communication ties arise from network dynamics (reciprocity, brokerage, and popularity) and from MPs’ participation in the parliamentary commissions, age, gender, and geographical area. Furthermore, there is a high degree of cross-party interactions in the Dutch MPs’ mentions Twitter network. Our results refute the existence of “echo chambers” in the Dutch MPs’ mentions Twitter network and support the hypothesis that social media can open up spaces for discussion among political parties. This is particularly important in fragmented consensus democracies where negotiation and coordination between parties to form coalitions is key.

Comparison of different neural networks in sentiment analysis of social media data

2021 ◽  
Author(s):  
Vadim Moshkin ◽  
Andrew Konstantinov ◽  
Nadezhda Yarushkina ◽  
Alexander Dyrnochkin
Keyword(s):  
Neural Networks ◽  
Social Media ◽  
Sentiment Analysis ◽  
Social Media Data ◽  
Media Data

scholarly journals Polarization in the contemporary political and media landscape

2020 ◽  
Author(s):  
Anne E Wilson ◽  
Victoria Parker ◽  
Matthew Feinberg
Keyword(s):  
Social Media ◽  
Political Polarization ◽  
Political Elites ◽  
Ideological Polarization ◽  
Societal Level ◽  
Polarization Phenomena ◽  
Partisan Media ◽  
Over Time

Political polarization is on the rise in America. Although social psychologists frequently study the intergroup underpinnings of polarization, they have traditionally had less to say about macro societal processes that contribute to its rise and fall. Recent cross-disciplinary work on the contemporary political and media landscape provides these complementary insights. In this paper, we consider the evidence for and implications of political polarization, distinguishing between ideological, affective, and false polarization. We review three key societal-level factors contributing to these polarization phenomena: the role of political elites, partisan media, and social media dynamics. We argue that institutional polarization processes (elites, media and social media) contribute to people’s misperceptions of division among the electorate, which in turn can contribute to a self-perpetuating cycle fueling animosity (affective polarization) and actual ideological polarization over time.

scholarly journals An Analysis of Fake Narratives on Social Media during 2019 Indonesian Presidential Election

2020 ◽  
Vol 36 (4) ◽  
pp. 351-368
Author(s):  
Vience Mutiara Rumata ◽  
◽  
Fajar Kuala Nugraha ◽  
Keyword(s):  
Social Media ◽  
Presidential Election ◽  
Political Polarization ◽  
Political Discussion ◽  
Fake News ◽  
Data Set ◽  
Political Situation ◽  
Radical Group ◽  
Time Frames ◽  
Election Studies

Social media become a public sphere for political discussion in the world, with no exception in Indonesia. Social media have broadened public engagement but at the same time, it creates an inevitable effect of polarization particularly during the heightened political situation such as a presidential election. Studies found that there is a correlation between fake news and political polarization. In this paper, we identify and the pattern of fake narratives in Indonesia in three different time frames: (1) the Presidential campaign (23 September 2018 -13 April 2019); (2) the vote (14-17 April 2019); (3) the announcement (21-22 May 2019). We extracted and analyzed a data-set consisting of 806,742 Twitter messages, 143 Facebook posts, and 16,082 Instagram posts. We classified 43 fake narratives where Twitter was the most used platform to distribute fake narratives massively. The accusation of Muslim radical group behind Prabowo and Communist accusation towards the incumbent President Joko Widodo were the two top fake narratives during the campaign on Twitter and Facebook. The distribution of fake narratives to Prabowo was larger than that to Joko Widodo on those three platforms in this period. On the contrary, the distribution of fake narratives to Joko Widodo was significantly larger than that to Prabowo during the election and the announcement periods. The death threat of Joko Widodo was top fake narratives on these three platforms. Keywords: Fake narratives, Indonesian presidential election, social media, political polarization, post.

scholarly journals Citizen Journalism and Public Participation in the Era of New Media in Indonesia: From Street to Tweet

2019 ◽  
Vol 7 (3) ◽  
pp. 79-90
Author(s):  
Iswandi Syahputra ◽  
Rajab Ritonga
Keyword(s):  
Social Media ◽  
Mass Media ◽  
New Media ◽  
Public Participation ◽  
Salient Feature ◽  
Political Polarization ◽  
Primary Data ◽  
Citizen Journalism ◽  
Independent Information ◽  
The Government

Citizen journalism was initially practiced via mass media. This is because citizens trusted mass media as an independent information channel, and social media like Twitter was unavailable. Following mass media’s affiliation to political parties and the rise of social media, citizens began using Twitter for delivering news or information. We dub this as citizen journalism from street to tweet. This study found that such process indicates the waning of mass media and the intensification of social media. Yet, the process neither strengthened citizen journalism nor increased public participation as it resulted in netizens experiencing severe polarization between groups critical and in support of the government instead. We consider this as a new emerging phenomenon caused by the advent of new media in the post-truth era. In this context, post-truth refers to social and political conditions wherein citizens no longer respect the truth due to political polarization, fake-news-producing journalist, hate-mongering citizen journalism, and unregulated social media activities. Primary data were obtained through in-depth interviews with four informants. While conversation data of netizens on Twitter were acquired from a Twitter conversation reader operated by DEA (Drone Emprit Academic), a big data system capable of capturing and analyzing netizen’s conversations, particularly on Twitter in real time. This study may have implications on the shift of citizen journalism due to its presence in the era of new media. The most salient feature in this new period is the obscurity of news, information, and opinions conveyed by citizens via social media, like Twitter.

scholarly journals Deep Neural Networks and Kernel Density Estimation for Detecting Human Activity Patterns from Geo-Tagged Images: A Case Study of Birdwatching on Flickr

2019 ◽  
Vol 8 (1) ◽  
pp. 45 ◽  
Author(s):  
Caglar Koylu ◽  
Chang Zhao ◽  
Wei Shao
Keyword(s):  
Neural Networks ◽  
Computer Vision ◽  
Social Media ◽  
Spatial Analysis ◽  
Citizen Science ◽  
Human Activity ◽  
Activity Patterns ◽  
Kernel Density ◽  
Human Activity Patterns ◽  
Vision Algorithm

Thanks to recent advances in high-performance computing and deep learning, computer vision algorithms coupled with spatial analysis methods provide a unique opportunity for extracting human activity patterns from geo-tagged social media images. However, there are only a handful of studies that evaluate the utility of computer vision algorithms for studying large-scale human activity patterns. In this article, we introduce an analytical framework that integrates a computer vision algorithm based on convolutional neural networks (CNN) with kernel density estimation to identify objects, and infer human activity patterns from geo-tagged photographs. To demonstrate our framework, we identify bird images to infer birdwatching activity from approximately 20 million publicly shared images on Flickr, across a three-year period from December 2013 to December 2016. In order to assess the accuracy of object detection, we compared results from the computer vision algorithm to concept-based image retrieval, which is based on keyword search on image metadata such as textual description, tags, and titles of images. We then compared patterns in birding activity generated using Flickr bird photographs with patterns identified using eBird data—an online citizen science bird observation application. The results of our eBird comparison highlight the potential differences and biases in casual and serious birdwatching, and similarities and differences among behaviors of social media and citizen science users. Our analysis results provide valuable insights into assessing the credibility and utility of geo-tagged photographs in studying human activity patterns through object detection and spatial analysis.

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