The use of disinformation and misinformation campaigns in the media has attracted much attention from academics and policy-makers. Multimodal analysis or the analysis of two or more semiotic systems-language, gestures, images, sounds, among others-in their interrelation and interaction is essential to understanding dis-/misinformation efforts because most human communication goes beyond just words. There is a confluence of many disciplines (e.g. computer science, linguistics, political science, communication studies) that are developing methods and analytical models of multimodal communication. This literature review brings research strands from these disciplines together, providing a map of the multi- and interdisciplinary landscape for multimodal analysis of dis-/misinformation. It records the substantial growth starting from the second quarter of 2020-the start of the COVID-19 epidemic in Western Europe-in the number of studies on multimodal dis-/misinformation coming from the field of computer science. The review examines that category of studies in more detail. Finally, the review identifies gaps in multimodal research on dis-/misinformation and suggests ways to bridge these gaps including future cross-disciplinary research directions. Our review provides scholars from different disciplines working on dis-/misinformation with a much needed bird's-eye view of the rapidly emerging research of multimodal dis-/misinformation.
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http://dx.doi.org/10.1098/rsos.230964 | DOI Listing |
JMIR Form Res
January 2025
Department of Computer Science, Purdue University, West Lafayett, IN, United States.
Background: Patient engagement is a critical but challenging public health priority in behavioral health care. During telehealth sessions, health care providers need to rely predominantly on verbal strategies rather than typical nonverbal cues to effectively engage patients. Hence, the typical patient engagement behaviors are now different, and health care provider training on telehealth patient engagement is unavailable or quite limited.
View Article and Find Full Text PDFJMIR Med Inform
January 2025
School of Software, Taiyuan University of Technology, Jingzhong, China.
Background: The prompt and accurate identification of mild cognitive impairment (MCI) is crucial for preventing its progression into more severe neurodegenerative diseases. However, current diagnostic solutions, such as biomarkers and cognitive screening tests, prove costly, time-consuming, and invasive, hindering patient compliance and the accessibility of these tests. Therefore, exploring a more cost-effective, efficient, and noninvasive method to aid clinicians in detecting MCI is necessary.
View Article and Find Full Text PDFNeurosurg Rev
January 2025
Department of Neurosurgery, The First Hospital of Jilin University, Changchun, China.
Atypical teratoid rhabdoid tumor (AT/RT) is a rare embryonal central nervous system tumor with a dismal prognosis that occurs mostly in early childhood. Since recent epidemiological and prognostic information is limited, we aimed to describe and analyze AT/RT-related incidences, temporal trends and prognostic factors. Incidence and survival data between 2001 and 2021 were extracted from the Surveillance, Epidemiology, and End Results (SEER) database.
View Article and Find Full Text PDFAnal Bioanal Chem
January 2025
Molecular Horizons and School of Chemistry and Molecular Bioscience, University of Wollongong, Wollongong, Australia.
The wide range of mass spectrometry imaging (MSI) technologies enables the spatial distributions of many analyte classes to be investigated. However, as each approach is best suited to certain analytes, combinations of different MSI techniques are increasingly being explored to obtain more chemical information from a sample. In many cases, performing a sequential analysis of the same tissue section is ideal to enable a direct correlation of multimodal data.
View Article and Find Full Text PDFJ Neurotrauma
January 2025
Division of Neuroscience, National Institute of Neurological Disorders and Stroke (NINDS), National Institutes of Health (NIH), Bethesda, Maryland, USA.
Effective team science requires procedural harmonization for rigor and reproducibility. Multicenter studies across experimental modalities (domains) can help accelerate translation. The Translational Outcomes Project in NeuroTrauma (TOP-NT) is a pre-clinical traumatic brain injury (TBI) consortium charged with establishing and validating noninvasive TBI assessment tools through team science.
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