Related Experiment Video
Updated: Jul 7, 2025
13:51
Cross-Modal Multivariate Pattern Analysis
Published on: November 9, 2011
20.0K
Multimodal analysis of disinformation and misinformation
Anna Wilson1, Seb Wilkes2, Yayoi Teramoto3
1Oxford School of Global and Area Studies, University of Oxford, Oxford OX1 2JD, UK.
Royal Society Open Science
|December 21, 2023
Summary
Multimodal analysis, combining language, images, and sounds, is crucial for understanding disinformation. This review maps the interdisciplinary field, highlighting computer science
Area of Science:
- Computer Science
- Linguistics
- Communication Studies
- Political Science
Background:
- Disinformation and misinformation campaigns are prevalent in media, attracting significant academic and policy attention.
- Effective analysis requires multimodal approaches, integrating language, gestures, images, and sounds, as human communication is inherently multimodal.
- Existing research involves a convergence of disciplines, including computer science, linguistics, political science, and communication studies.
Purpose of the Study:
- To synthesize research on multimodal analysis of disinformation and misinformation across various disciplines.
- To map the interdisciplinary landscape and identify emerging trends in multimodal dis-/misinformation research.
- To pinpoint research gaps and suggest future cross-disciplinary research directions.
Main Methods:
- Literature review synthesizing studies from computer science, linguistics, political science, and communication studies.
- Analysis of the growth and focus of multimodal dis-/misinformation research, particularly from computer science.
- Identification of research gaps and future research avenues.
Main Results:
- A substantial increase in multimodal dis-/misinformation studies, especially from computer science, began in early 2020.
- The review provides a comprehensive overview of the multi- and interdisciplinary research landscape.
- Key research gaps in multimodal dis-/misinformation analysis have been identified.
Conclusions:
- Multimodal analysis is essential for understanding contemporary disinformation and misinformation.
- The field is rapidly growing, with significant contributions from computer science.
- Further interdisciplinary collaboration is needed to address identified research gaps and advance the field.
More Related Videos
12:55Multimodal Protocol for Assessing Metacognition and Self-Regulation in Adults with Learning Difficulties
Published on: September 27, 2020
8.5K
08:51Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts
Published on: September 20, 2024
1.3K
Related Concept Videos
02:16Stereotype Content Model
14.7K
The Stereotype Content Model (SCM) was first proposed by Susan Fiske and her colleagues (Fiske, Cuddy, Glick & Xu, 2002; see also Fiske, 2012 and Fiske, 2017). The SCM specifies that when someone encounters a new group, they will stereotype them based on two metrics: warmth—or that group’s perceived intent, and how likely they are to provide help or inflict harm—and competence—or their ability to carry out that objective. Depending on the warmth-competence...
14.7K
01:07What is a Mode?
18.9K
The mode is one of the commonly used measures of a central tendency. It is defined as the most frequent value in a data set.
There can be more than one mode in a data set if multiple values have the same highest frequency. For instance, suppose that the Statistics exam scores of 20 students are: 50; 53; 59; 59; 63; 63; 72; 72; 72; 72; 72; 76; 78; 81; 83; 84; 84; 84; 90; 93. Here, the mode is 72, as it occurs most frequently, five times.
A data set with two modes is called bimodal. For example,...
There can be more than one mode in a data set if multiple values have the same highest frequency. For instance, suppose that the Statistics exam scores of 20 students are: 50; 53; 59; 59; 63; 63; 72; 72; 72; 72; 72; 76; 78; 81; 83; 84; 84; 84; 90; 93. Here, the mode is 72, as it occurs most frequently, five times.
A data set with two modes is called bimodal. For example,...
18.9K
01:01Group Polarization
34.3K
Group polarization is the strengthening of an original group attitude following the discussion of views within a group (Teger & Pruitt, 1967). That is, if a group initially favors a viewpoint, after discussion the group consensus is likely a stronger endorsement of the viewpoint. Conversely, if the group was initially opposed to a viewpoint, group discussion would likely lead to stronger opposition.
34.3K
01:09Types of Skewness
11.6K
If the frequency distribution of a data set is more inclined towards smaller or larger values, the distribution is said to be skewed. If data values are skewed to the right, then the distribution is called positively skewed. Conversely, if the plot is skewed to the left, the distribution is called negatively skewed.
For instance, in the middle of a pandemic, the geographical distribution of vaccine coverage may be positively skewed towards populations in the global north countries. However,...
For instance, in the middle of a pandemic, the geographical distribution of vaccine coverage may be positively skewed towards populations in the global north countries. However,...
11.6K
01:31Confirmation Biases
5.5K
The confirmation bias is the tendency to focus on information that confirms our existing beliefs and ignore information that is inconsistent with our expectations. For example, if you think that your professor is not very nice, you notice all of the instances of rude behavior exhibited by the professor while ignoring the countless pleasant interactions he is involved in on a daily basis. Have you ever fallen prey to the confirmation bias, either as the source or target of such bias?
5.5K
00:52Social Proof
27.7K
Social proof is a form of persuasion based on comparison and conformity. People compare their behavior and actions to what others are doing and will change to conform to do what their peers do.
27.7K