According to both professional journalists and news users, news should be While a great deal of research that treats relevance as co-constructed starts from the text of news stories, this paper asks how explicitly construct the (ir)relevance of particular news reports, taking a language-centered lens to open-ended survey responses. This paper makes a methodological argument in favor of a language-centered approach to open-ended survey data. Given the ubiquity of online surveys in many social science disciplines, the present paper provides an example of how this approach can deepen our understanding of survey responses. We find that news users construct relevance at varying scales, using a number of linguistic strategies of self-reference. Those who said they found the story they saw relevant used pronouns with a different distribution than those who did not, and these differences exceeded chance. In general, those who referred to themselves as members of larger collectivities were more likely to say they found a news story relevant, suggesting that relevance is discursively constructed in part through practices of self-reference.
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http://dx.doi.org/10.1016/j.pragma.2020.10.001 | DOI Listing |
Proc Natl Acad Sci U S A
January 2025
Department of Communication, Stanford University, Stanford, CA 94305.
There is a widespread perception that China's digital censorship distances its people from the global internet, and the Chinese Communist Party, through state-controlled media, is the main gatekeeper of information about foreign affairs. Our analysis of narratives about the Russo-Ukrainian War circulating on the Chinese social media platform Weibo challenges this view. Comparing narratives on Weibo with 8.
View Article and Find Full Text PDFJMIR Infodemiology
January 2025
Computational Social Science DataLab, University Institute of Research for Sustainable Social Development (INDESS), University of Cadiz, Jerez de la Frontera, Spain.
Background: During the COVID-19 pandemic, social media platforms have been a venue for the exchange of messages, including those related to fake news. There are also accounts programmed to disseminate and amplify specific messages, which can affect individual decision-making and present new challenges for public health.
Objective: This study aimed to analyze how social bots use hashtags compared to human users on topics related to misinformation during the outbreak of the COVID-19 pandemic.
Objective: The content shared on social media may cause secondary traumatic stress (STS) symptoms. The aim of this study is to evaluate the severity of social media related STS and the associated factors in university students who were not directly affected by the February 2023 earthquakes.
Method: In total, 436 university students completed an online survey including the Secondary Traumatic Stress Scale for Social Media Users (STSS-SM), the Bergen Social Media Addiction Scale (BSMAS), the Depression Anxiety Stress Scales (DASS-42), and demographic information and questions regarding social media use preferences after the earthquake.
Genetics
January 2025
Dept. of Genetics, Stanford University, Stanford CA 94305-5120, USA.
The Candida Genome Database (CGD; www.candidagenome.org) is unique in being both a model organism database and a fungal pathogen database.
View Article and Find Full Text PDFPLoS One
January 2025
Seoul National University, Seoul, Republic of Korea.
How can we recommend items to users utilizing multiple types of user behavior data? Multi-behavior recommender systems leverage various types of user behavior data to enhance recommendation performance for the target behavior. These systems aim to provide personalized recommendations, thereby improving user experience, engagement, and satisfaction across different applications such as e-commerce platforms, streaming services, news websites, and content platforms. While previous approaches in multi-behavior recommendation have focused on incorporating behavioral order and dependencies into embedding learning, they often overlook the nuanced importance of individual behaviors in shaping user preferences during model training.
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