Relevance & Research Question

The Iron Swords War, which erupted in October 2023, has witnessed the emergence of Telegram as a significant news platform in Israel. The war was marked by the proliferation of alternative news channels and their exponential growth in followers. This study analyzes items from the nine most-followed independent Telegram news channels during the first year of the war. It examines how content creators address ethical and professional dilemmas traditionally associated with conventional journalism, as reflected in their posts. The main research questions are: What types of professional and ethical dilemmas emerge in these Telegram channels, and how do these align with or diverge from conventional journalistic dilemmas?

Methods & Data

The study employs qualitative content analysis of posts from selected Telegram news channels, sampled between October 2023 and September 2024, beginning from the October 2023. A systematic random sampling yielded 300 posts (distributed equally across channels), which were thematically analyzed to identify explicit and implicit references to ethical and professional dilemmas. These were subsequently compared with the Israeli Journalists Association's ethical code of conduct to identify patterns of convergence and divergence.

Results

Content creators across all analyzed channels consistently grappled with professional and ethical dilemmas throughout the study period. The primary tension emerged between respecting victims' families' sensitivities and fulfilling the professional obligation to inform the public. A secondary dilemma involved balancing journalistic transparency against national security considerations. Notably, these channels demonstrated limited adherence to traditional journalistic practices regarding error correction and fact-verification protocols.

Added Value

This research contributes to the emerging scholarship on Telegram as a news platform and presents the first systematic analysis of its role during active warfare. By examining content creators' navigation of professional and ethical challenges, it illuminates the evolving nature of wartime journalism in digital spaces. The findings enhance our understanding of both crisis reporting and digital alternative journalism while raising important questions about accountability in emerging news platforms.

Relevance & Research Question: In today’s digital world, news websites, online encyclopedias and other knowledge platforms are crucial for many to gain access to reliable sources of information. On Wikipedia — one of the most frequented web pages globally — every edit to an article regarding an event must cite a reliable source. Therefore, we hypothesise that edits on Wikipedia pages can be predicted using related news articles. Understanding the process of updating Wikipedia pages via news articles gives insight into how information, including misinformation, integrates into a platform used by the public as a reliable source of information. We consider pages on armed conflicts as a case study, as these are often fast-developing events with much information need.

Methods & Data: We examine the Russo-Ukrainian War, the Mali War, and the Sudanese Civil War over a 4-year time period. We use an API to collect online news articles as well as Wikipedia articles and edits. The Wikipedia pages and news articles are linked to each other using keywords which appear in both sources. We then use three machine learning models to predict the number of Wikipedia edits per day using the news sources. To examine how news articles relate to edits on a more granular level, we introduce a novel metric called the difference measure.

Results: We find that all the machine learning models perform well, producing an MSE score of less than one that is stable across different wars and pages. Of notable interest is the fact that the titles of BBC articles act as sufficient predictors. Additionally, the difference measure is able to supplement traditional controversy measures by identifying excessive edits in response to real-life events.

Added Value: This framework is the first that forecasts when information on Wikipedia is likely to change on a granular scale using exogenous sources. Hence, it could be used as an early warning tool of a page’s vulnerability on any online platform that is regularly updated by an active user community. This gives the moderators of the community more time to implement protection methods to preserve the quality of information.

Relevance & Research Question

This article investigates the link between territorial conflict and digital disputes, focusing on Wikipedia during the Russo-Ukrainian war. Wikipedia, one of the most visited websites, is regarded as a "consensus truth" in Western societies and has increasing significance due to large language models that rely heavily on its content, making it a key data source for developing AI tools. Despite its role as a reliable information source, the platform's open-access nature makes it vulnerable to manipulation and attempts to influence public opinion. Therefore, we hypothesize that the 2022 invasion of Ukraine led to heightened attention and disputes on Wikipedia, particularly in articles about contested Ukrainian regions.

Methods & Data

To analyse the digital impact of territorial disputes, we use Wikipedia data and classify regions into three groups: disputed Ukrainian Oblasts, undisputed Ukrainian Oblasts, and Polish Voivodeships. Disputed Oblasts are defined as areas where Russian forces gained territory, or Russia annexed a
region without Ukraine accepting the claim.

We develop a custom Natural Language Processing (NLP) method to identify dispute edits on the Wikipedia pages of disputed Ukrainian Oblasts.

In addition to using the number of dispute edits as a conflict proxy, we quantify the 2022 invasion’s impact with two further metrics: the number of revisions (proxying editor community attention) and the number of identity reverts (indicating generalized disputes). We use a difference-in-difference (DiD) regression setup to measure the invasion's effect on all three dependent variables.

Results

We find a significant increase in attention and dispute on articles about disputed Ukrainian regions compared to those about undisputed regions. Furthermore, we find that dispute edits frequently involve debates over the Ukrainian versus Russian spelling of place names and discussions of national identity, reflecting the broader conflict.

Added Value

The results presented here confirm our initial assumption that territorial conflicts are spreading into the digital realm in the case of the Russo-Ukrainian war. While research exists on other media, such as social media, newspapers, and mass media, our findings shed light on a previously overlooked digital conflict zone: attempts to change the identity of disputed regions on Wikipedia.