Related papers: Evaluating Twitter's Algorithmic Amplification of …
In a pre-registered algorithmic audit, we found that, relative to a reverse-chronological baseline, Twitter's engagement-based ranking algorithm amplifies emotionally charged, out-group hostile content that users say makes them feel worse…
The proliferation of online misinformation has emerged as one of the biggest threats to society. Considerable efforts have focused on building misinformation detection models, still the perils of misinformation remain abound. Mitigating…
There has been much effort on studying how social media sites, such as Twitter, help propagate information in different situations, including spreading alerts and SOS messages in an emergency. However, existing work has not addressed how to…
Influential users have great potential for accelerating information dissemination and acquisition on Twitter. How to measure the influence of Twitter users has attracted significant academic and industrial attention. Existing influential…
Despite the prevalence of adverse pregnancy outcomes such as miscarriage, stillbirth, birth defects, and preterm birth, their causes are largely unknown. We seek to advance the use of social media for observational studies of pregnancy…
In this paper, we investigate the issue of detecting the real-life influence of people based on their Twitter account. We propose an overview of common Twitter features used to characterize such accounts and their activity, and show that…
Online social networks serve as major platforms for disseminating both real and fake news. Many users--intentionally or unintentionally--spread harmful content, misinformation, and rumors in domains such as politics and business.…
Online hate messaging is a pervasive issue plaguing the well-being of social media users. This research empirically investigates a novel theory positing that online hate may be driven primarily by the pursuit of social approval rather than…
In the digital environment, human attention is frequently guided by cognitive heuristics rather than deliberate evaluation. Since low-credibility narratives often lack substantive factual evidence, their diffusion disproportionally relies…
Nowadays, people from all around the world use social media sites to share information. Twitter for example is a platform in which users send, read posts known as tweets and interact with different communities. Users share their daily…
In today's digital landscape, the proliferation of conspiracy theories within the disinformation ecosystem of online platforms represents a growing concern. This paper delves into the complexities of this phenomenon. We conducted a…
Models of contagion dynamics, originally developed for infectious diseases, have proven relevant to the study of information, news, and political opinions in online social systems. Modelling diffusion processes and predicting viral…
Social media platforms empower us in several ways, from information dissemination to consumption. While these platforms are useful in promoting citizen journalism, public awareness etc., they have misuse potentials. Malicious users use them…
Artificial intelligence is changing the way we create and evaluate information, and this is happening during an infodemic, which has been having dramatic effects on global health. In this paper we evaluate whether recruited individuals can…
Social media algorithms are thought to amplify variation in user beliefs, thus contributing to radicalization. However, quantitative evidence on how algorithms and user preferences jointly shape harmful online engagement is limited. I…
The analysis of social networks enables the understanding of social interactions, polarisation of ideas, and the spread of information and therefore plays an important role in society. We use Twitter data - as it is a popular venue for the…
Misinformation on social media presents a major threat to modern societies. While previous research has analyzed the virality across true and false social media posts, not every misleading post is necessarily equally viral. Rather,…
The problem of ideology detection is to study the latent (political) placement for people, which is traditionally studied on politicians according to their voting behaviors. Recently, more and more studies begin to address the ideology…
In addition to more personalized content feeds, some leading social media platforms give a prominent role to content that is more widely popular. On Twitter, "trending topics" identify popular topics of conversation on the platform, thereby…
Social media classification tasks (e.g., tweet sentiment analysis, tweet stance detection) are challenging because social media posts are typically short, informal, and ambiguous. Thus, training on tweets is challenging and demands…