Armed conflict presents a multitude of risks to civilians, prisoners of war and others caught in the middle of hostilities. Harmful information spreading on social media compounds such risks in a variety of tangible ways, from potentially influencing acts that cause physical harm to undermining a person's financial stability, contributing to psychological distress, spurring social ostracization and eroding societal trust in evidentiary standards, among many others. Despite this span of risks, no typology exists that maps the full range of such harms. This article attempts to fill this gap, proposing a typology of harms related to the spread of harmful information on social media platforms experienced by persons affected by armed conflict. Developed using real-world examples, it divides potential harm into five categories: harms to life and physical well-being, harms to economic or financial well-being, harms to psychological well-being, harms to social inclusion or cultural well-being, and society-wide harms. After detailing each component of the typology, the article concludes by laying out several implications, including the need to view harmful information as a protection risk, the importance of a conflict-specific approach to harmful information, the relevance of several provisions under international law, and the possible long-term consequences for societies from harmful information.

The information used for this typology is based entirely on open-source reporting covering acts that occurred during armed conflict and that were seemingly related to identified harmful information on social media platforms or messaging applications. The authors did not verify any reported incidents or information beyond what was included in cited sources. Throughout the article, sources have been redacted from citations where there is a risk of reprinting harmful information or further propagating it, and where redaction was necessary to avoid the impression that the authors were attributing acts to particular groups or actors.

This research examines how Beijing uses social media to publicize donations and engage in nation branding as it responds to the global backlash sparked by Covid-19. It argues that self-reports of medical donations aim to enhance China’s national brand, leading to an expectation that reports about donations will primarily target countries more severely affected by the virus. To test its claims, the research analyzes over 55,000 tweets published by Chinese diplomatic missions. The results—controlled for Chinese donation exports—show a positive and significant relationship between self-reports of medical donations and the host’s spread of Covid-19. In contrast, donations are not correlated with political or economic allies. A comparison of government (CCP, ministries, etc.) and non-government donors (immigrants, firms, etc.) shows that only donations by the latter are positively correlated with the spread of the virus. This research advances our knowledge of Chinese diplomats’ online political behavior.

Small claim. Regulation 861/2007 (European Small Claims Procedure Regulation). Unfair term in terms of service agreement. Breach of contract through shadowbanning. Infringement on Articles 12 and 17 of Regulation 2022/2065 (Digital Services Act).

Originating from a unique partnership between data scientists (datavaluepeople) and peacebuilders (Build Up), this commentary explores an innovative methodology to overcome key challenges in social media analysis by developing customized text classifiers through a participatory design approach, engaging both peace practitioners and data scientists. It advocates for researchers to focus on developing frameworks that prioritize being usable and participatory in field settings, rather than perfect in simulation. Focusing on a case study investigating the polarization within online Christian communities in the United States, we outline a testing process with a dataset consisting of 8954 tweets and 10,034 Facebook posts to experiment with active learning methodologies aimed at enhancing the efficiency and accuracy of text classification. This commentary demonstrates that the inclusion of domain expertise from peace practitioners significantly refines the design and performance of text classifiers, enabling a deeper comprehension of digital conflicts. This collaborative framework seeks to transition from a data-rich, analysis-poor scenario to one where data-driven insights robustly inform peacebuilding interventions.

This article examines the information sharing behavior of U.S. politicians and the mass public by mapping the ideological sharing space of political news on social media. As data, we use the near-universal currency of online information exchange: web links. We introduce a methodological approach and software to unify the measurement of ideology across social media platforms by using sharing data to jointly estimate the ideology of news media organizations, politicians, and the mass public. Empirically, we show that (1) politicians who share ideologically polarized content share, by far, the most political news and commentary and (2) that the less competitive elections are, the more likely politicians are to share polarized information. These results demonstrate that news and commentary shared by politicians come from a highly unrepresentative set of ideologically extreme legislators and that decreases in election pressures (e.g., by gerrymandering) may encourage polarized sharing behavior.

We introduce a dynamic dataset of all communications by state election officials (EOs) on social media during the 2022 election cycle and develop metrics to assess the effectiveness of trust-building strategies on voter confidence. We employ quantitative manual content analysis of 10,000 organic posts from 118 state EOs’ accounts on Facebook, Instagram, and Twitter between September 10 and November 30, 2022, and code for the presence of variables that measure EOs’ efforts to combat misinformation and build trusted networks of communications. The measures we present here address two questions: (1) How much coordination was there among states in terms of incorporating the #TrustedInfo2022 campaign, promoted by the National Association of Secretaries of State, in their social media communications, and (2) How much of states’ social media communications explicitly signaled that EOs are trusted sources of information? We demonstrate the applicability of our data on research that evaluates the impact of trust-building campaigns on voter confidence in elections, which is grounded on theories of deliberative democracy and democratic listening.

We analyze a cache of tweets from partisan users concerning the confirmation hearings of Justices Brett Kavanaugh, Amy Coney Barrett, and Ketanji Brown Jackson. Using these original data, we investigate how Twitter users with partisan leanings interact with judicial nominations and confirmations. We find that these users tend to exhibit behavior consistent with offline partisan dynamics. Our analysis reveals that Democrats and Republicans express distinct emotional responses based on the alignment of nominees with their respective parties. Additionally, our study highlights the active participation of partisans in promoting politically charged topics throughout the confirmation process, starting from the vacancy stage.