YouTube video by kindlespark

YouTube video by Jonathan Pie

YouTube video by Jonathan Pie

The EPJ portal gives you access to EPJA, EPJB, EPJC, EPJD, EPJE, EPJAP, EPJST, EPJH, EPJPlus, EPJ Data Science, EPJ Photovoltaics, EPJ Web of Conferences, EPJ Nuclear Sciences & Technologies, EPJ ...

In today’s digital age, images have become powerful tools for politicians to engage with their voters on social media platforms. Visual content possesses a unique emotional appeal that often leads to ...

This study investigates how the majority group influences individual judgment formation and expression in anonymous, spontaneous online conversations. Drawing on theories of social conformity and anti-conformity, we analyze everyday dilemmas discussed on social media. First, using digital traces to operationalize judgments, we measure the conversations’ disagreement and apply Bayesian regression to capture shifts of judgments formation before and after the group’s exposure. Then we analyze changes in judgment expression with a linguistic analysis of the motivations associated with each judgment. Results show anti-conformity behaviors: individuals preserve the majority’s positive or negative orientation of judgments but diverge from its stance, with persuasive language increasing post-disclosure. Our findings highlight how online environments reshape social influence compared to offline contexts.

Malicious actors exploit social media to inflate stock prices, sway elections, spread misinformation, and sow discord. To these ends, they employ tactics that include the use of inauthentic accounts a...

The EPJ portal gives you access to EPJA, EPJB, EPJC, EPJD, EPJE, EPJAP, EPJST, EPJH, EPJPlus, EPJ Data Science, EPJ Photovoltaics, EPJ Web of Conferences, EPJ Nuclear Sciences & Technologies, EPJ ...

The spread of misinformation poses a threat to the social media ecosystem. Effective countermeasures to mitigate this threat require that social media platforms be able to accurately detect low-credib...

News sources play a central role in democratic societies by shaping political and social discourse through specific topics, viewpoints and voices. Understanding these dynamics is essential for assessing whether the media landscape offers a balanced and fair account of public debate. In earlier work, we introduced a pipeline that, given a news corpus, i) uses a hybrid human–machine approach to identify the range of viewpoints expressed about a given topic, and ii) classifies relevant claims with respect to the identified viewpoints, defined as sets of semantically and ideologically congruent claims (e.g., positions arguing that immigration positively impacts the UK economy). In this paper, we improve this pipeline by i) fine-tuning Large Language Models (LLMs) for viewpoint classification and ii) enriching claim representations with semantic descriptions of relevant actors drawn from Wikidata. We evaluate our approach against alternative solutions on a benchmark centred on the UK immigration debate. Results show that while both mechanisms independently improve classification performance, their integration yields the best results, particularly when using LLMs capable of processing long inputs.

YouTube video by Jonathan Pie

YouTube video by Jonathan Pie

YouTube video by Jonathan Pie

YouTube video by Jonathan Pie

YouTube video by Jonathan Pie

YouTube video by Jonathan Pie