Political polarization on the digital sphere poses a real challenge to many democracies around the world. Although the issue has received some scholarly attention, there is a need to improve the conceptual precision in the increasingly blurry debate. The use of computational communication science approaches allows us to track political conversations in a fine-grained manner within their natural settings – the realm of interactive social media. The present study combines different algorithmic approaches to studying social media data in order to capture both the interactional structure and content of dynamic political talk online. We conducted an analysis of political polarization across social media platforms (analyzing Facebook, Twitter, and WhatsApp) over 16 months, with close to a quarter million online contributions regarding a political controversy in Israel. Our comprehensive measurement of interactive political talk enables us to address three key aspects of political polarization: (1) interactional polarization – homophilic versus heterophilic user interactions; (2) positional polarization – the positions expressed, and (3) affective polarization – the emotions and attitudes expressed. Our findings indicate that political polarization on social media cannot be conceptualized as a unified phenomenon, as there are significant cross-platform differences. While interactions on Twitter largely conform to established expectations (homophilic interaction patterns, aggravating positional polarization, pronounced inter-group hostility), on WhatsApp, de-polarization occurred over time. Surprisingly, Facebook was found to be the least homophilic platform in terms of interactions, positions, and emotions expressed. Our analysis points to key conceptual distinctions and raises important questions about the drivers and dynamics of political polarization online.

Much is known about the negative consequences of exposure to ethnic/racial stereotypes in the media, on White audiences. However, there is substantially less research addressing the effects of such media content on members of racial/ethnic minority groups. The present study examines this question using repeated cross-sectional national level data alongside a content analysis of top prime time shows between 1987 and 2008. Multi-level modeling analyses reveal that media representations of Latinos and Blacks were associated with Latinos’ and Blacks’ ingroup attitudes, however effects on attitudes towards Whites were not significant.

In light of past studies’ findings that male politicians usually receive more salient coverage in the traditional media, this study examines whether social media provide a more equal opportunity platform for female politicians to generate users’ engagement and mobilize their supporters. Our study focuses on male versus female politicians’ ability to engage social media users during an election campaign, using the Israeli 2015 campaign as our case study. Female politicians’ posts generated significantly more user engagement in terms of the number of Likes and Shares in comparison to male politicians, while generating the same number of participants in their discussions. The results remained significant in a multilevel analysis, while controlling for other factors such as the politician’s age, party, ranking, and number of posts published by the politician. In terms of the posts’ content, an examination of their rhetoric did not reveal gender differences in their usage of authoritative voice, facts, and the use of emotions in their messages; however, analysis of the topics did suggest differences in the issues raised by male versus female politicians. The evidence strongly indicates that social media provides greater opportunities for female politicians to promote themselves and improve their status in the political power play.

Given the scale of digital communication, researchers face a painful trade-off between powerful, scalable computational strategies, and the theoretical sensitivity offered by small-scale manual analyses. Especially in the study of natural discourse on digital media, the interactive, ever-evolving stream of conversations across multiple platforms regularly defies efforts to obtain well-defined samples of manageable size, while their linguistic variability imposes major limitations upon the accuracy of automated tools. In this paper, we draw upon recent advances in computational text analysis to develop a hybrid approach to the deductive analysis of large-scale digital discourse, which combines the algorithmic extraction of coherent, recurrent patterns with a manual coding of identified patterns. The approach scales up to treat millions of texts at minimal added human effort, while affording researchers close control over the process of theory-guided classification. We demonstrate the power of Hybrid Content Analysis by studying polarization in a quarter of a million contributions from cross-platform interactive social media discourse about a controversial incident.

Antagonists’ images in the international news media can play a significant role in determining their level of political success in the international arena, which explains why so many political actors invest considerable resources in public diplomacy. The goal of the present study is to explain the level of success that various actors (countries and non-state actors) have in promoting their preferred frames about terror to the international news media. Four types of explanatory variables are proposed, divided into context and focal event factors. Context factors include the political values and policy proximity between the country attacked (the victimized country) and a country whose news media have been targeted for influence (the target country), as well as the target country’s experience in dealing with terror. Focal event factors refer to the nature of the trigger events that generate news coverage of terrorism. Apart from one exception (the policy proximity), all of the hypotheses were confirmed. The findings indicated that focal event factors have the most significant effect on the way foreign media covers conflicts and that, when it comes to coverage of terrorism, journalists are more interested in constructing a dramatic story than putting the events into a more general political context.

The current paper analyzes the recent wave of violence in the Israeli–Palestinian conflict, and especially the occurrences on the Gazan border since March 2018, from a less covered point of view – the battle over the image and narrative. The Palestinians’ main tools of resistance during this wave of violence have been civilian protests near the fence and the launching of explosive kites and balloons across the border – causing massive damage to Israeli agriculture. We argue that the Palestinians had waged a sophisticated Image War on the border of Gaza, and while they are performing, the world is watching.