The Spread of Disinformation on the Web: An Examination of Memes on Social Networking

Dupuis, Marc; Williams, Andrew

doi:10.1109/smartworld-uic-atc-scalcom-iop-sci.2019.00256

Cited by 14 publications

(13 citation statements)

References 29 publications

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“…Virality. Recent research showed that image memes have become a prominent way for social media users to communicate complex concepts [21,92]. Consistent with previous meme studies [86], we use the number of times a meme is shared on social media as an indicator of its virality; viral memes appear at a high frequency on online services and are shared, transformed, and imitated by many people.…”

Section: Memesmentioning

confidence: 64%

“…Researchers recently started studying image memes and how online users interact with them. Dupuis et al performed a survey to identify personality traits of users that are more likely to share image memes containing disinformation [21]. Crovitz and Moran provided a qualitative analysis of image memes used as a vehicle of disinformation on social media [17].…”

Section: Related Workmentioning

confidence: 99%

“…For instance, the Black Lives Matter movement [33] has made extensive use of memes, often as response to racism online or to gather broader support [45]. At the same time, disinformation actors routinely exploit memes to promote false narratives on political scandals [54] and weaponize them to spread propaganda as well as manipulate public opinion [21,87,91,92].…”

Section: Introductionmentioning

confidence: 99%

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Dissecting the Meme Magic: Understanding Indicators of Virality in Image Memes

Ling

AbuHilal

Blackburn

et al. 2021

Preprint

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Despite the increasingly important role played by image memes, we do not yet have a solid understanding of the elements that might make a meme go viral on social media. In this paper, we investigate what visual elements distinguish image memes that are highly viral on social media from those that do not get re-shared, across three dimensions: composition, subjects, and target audience. Drawing from research in art theory, psychology, marketing, and neuroscience, we develop a codebook to characterize image memes, and use it to annotate a set of 100 image memes collected from 4chan's Politically Incorrect Board (/pol/). On the one hand, we find that highly viral memes are more likely to use a close-up scale, contain characters, and include positive or negative emotions. On the other hand, image memes that do not present a clear subject the viewer can focus attention on, or that include long text are not likely to be re-shared by users.We train machine learning models to distinguish between image memes that are likely to go viral and those that are unlikely to be re-shared, obtaining an AUC of 0.866 on our dataset. We also show that the indicators of virality identified by our model can help characterize the most viral memes posted on mainstream online social networks too, as our classifiers are able to predict 19 out of the 20 most popular image memes posted on Twitter and Reddit between 2016 and 2018. Overall, our analysis sheds light on what indicators characterize viral and non-viral visual content online, and set the basis for developing better techniques to create or moderate content that is more likely to catch the viewer's attention.

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Section: Memesmentioning

confidence: 64%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Dissecting the Meme Magic: Understanding Indicators of Virality in Image Memes

Ling

AbuHilal

Blackburn

et al. 2021

Preprint

View full text Add to dashboard Cite

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“…Each study introduces ad hoc definitions, leading to conflicts or overlaps. For example, Amazeen and Bucy (2019), Dupuis and Williams (2019), and HLEG (2018) consider disinformation as an umbrella term in their studies, whereas Wardle and Derekshan (2017) examine it as a narrower term, adopting “information disorder” as hypernym. The lack of a unified categorization framework and vocabulary creates a fragmented news ecosystem which motivated us to compare and combine existing approaches and draft a typology.…”

Section: Problem Definition Scope and Methodology Designmentioning

confidence: 99%

A systematic literature review on disinformation: Toward a unified taxonomical framework

Kapantai

Christopoulou

Berberidis

et al. 2020

New Media & Society

144

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The scale, volume, and distribution speed of disinformation raise concerns in governments, businesses, and citizens. To respond effectively to this problem, we first need to disambiguate, understand, and clearly define the phenomenon. Our online information landscape is characterized by a variety of different types of false information. There is no commonly agreed typology framework, specific categorization criteria, and explicit definitions as a basis to assist the further investigation of the area. Our work is focused on filling this need. Our contribution is twofold. First, we collect the various implicit and explicit disinformation typologies proposed by scholars. We consolidate the findings following certain design principles to articulate an all-inclusive disinformation typology. Second, we propose three independent dimensions with controlled values per dimension as categorization criteria for all types of disinformation. The taxonomy can promote and support further multidisciplinary research to analyze the special characteristics of the identified disinformation types.

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“…Dupuis and his colleagues A survey was done to determine the personality traits of individuals who are prone to post picture memes with false information. 10 Crovitz and Moran 11 conducted a qualitative examination of visual memes used as a type of social media deception. Zannettou et al 12 propose a large-scale quantitative measurement of picture meme spread on the Web, where the small, polarized community subreddit is racist and loathed on mainstream social media like Twitter and Reddit.…”

Section: Memes Methodsmentioning

confidence: 99%

Meme Generation Using Deep Neural Network to Engage Viewers on Social Media

Alandjani

Bouk

2021

Yanbu Journal of Engineering and Science

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In today’s technological era, most new generations are using memes because the phenomenon of memes is rapidly gaining in popularity. In addition, various modifications and parodies use social and cultural phenomena to transform an original idea into another. Virus memes are sometimes called virus scripts and are the most widespread on the Internet. However, there is little scientific evidence for this assumption. This paper presents an innovative method to generate memes using machine learning algorithms that match the users’ humorous and relevant captions. The VGG16 network is used to return the embedded image. We have collected feedback from citizens of more than five countries and received 37 feedbacks to find humorous memes. The obtained results from feedback are very encouraging from the generated memes.

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The Spread of Disinformation on the Web: An Examination of Memes on Social Networking

Cited by 14 publications

References 29 publications

Dissecting the Meme Magic: Understanding Indicators of Virality in Image Memes

Dissecting the Meme Magic: Understanding Indicators of Virality in Image Memes

A systematic literature review on disinformation: Toward a unified taxonomical framework

Meme Generation Using Deep Neural Network to Engage Viewers on Social Media

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