Machine learning techniques and older adults processing of online information and misinformation: A covid 19 study

Choudrie, Jyoti; Banerjee, Snehasish; Kotecha, Ketan; Walambe, Rahee; Karende, Hema; Ameta, Juhi

doi:10.1016/j.chb.2021.106716

Cited by 105 publications

(85 citation statements)

References 69 publications

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“…It refers to forms of factually inaccurate information that is propagated inadvertently [25], often considered as an honest mistake [4]. Some studies use misinformation as an umbrella term when referring to false information circulating online [1]. Misinformation, disinformation, and fake news are three distinct concepts, although similar characteristics are detectable in the definitions used.…”

Section: Overview Of Key Conceptsmentioning

confidence: 99%

“…Other potentially conceptually overlapping with misinformation are propaganda, conspiracy theories, and false rumors [23]. Disinformation, in addition to the falseness of the information, includes the purposeful intention by the sender or information provider, and thus distributed, asserted, or disseminated-often online-to mislead, deceive, or confuse [1,[24][25][26][27]. The communication of misinformation has a social function: (1) it is comprehended as a type of collective sense-making, and (2) it subverts outgroups or rivals [23].…”

Section: Overview Of Key Conceptsmentioning

confidence: 99%

“…As a concept, fake news avoids any agreed definition [1,30], although it goes back centuries, and it became a buzzword particularly after the 2016 presidential elections in the United States [28,29]. Therefore, it currently has a political flavor [1,3,17], but vaccination, nutrition, and the stock market are also topics of fake news [3]. The research on fake news has focused a lot on the "fakeness," i.e., the deception as well as the motivation of the deceivers [29].…”

Section: Overview Of Key Conceptsmentioning

confidence: 99%

“…Misinformation has an adverse societal impact because it accelerates propaganda, creates anxiety, induces fear, and sways public opinion [1]. Unfortunately, its speed, geographical reach, depth, and breadth exceed those of information, yet humans seldom detect misinformation [1,17].…”

Section: Overview Of Key Conceptsmentioning

confidence: 99%

“…Across disciplines, there is an increasing interest in misinformation, which is an umbrella term referring to false information circulating online [1]. However, there is also limited understanding of why certain individuals, societies, and institutions are more vulnerable to disinformation, i.e., the intentional spread of misinformation [2].…”

Section: Introductionmentioning

confidence: 99%

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Text Analysis Methods for Misinformation–Related Research on Finnish Language Twitter

et al. 2021

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The dissemination of disinformation and fabricated content on social media is growing. Yet little is known of what the functional Twitter data analysis methods are for languages (such as Finnish) that include word formation with endings and word stems together with derivation and compounding. Furthermore, there is a need to understand which themes linked with misinformation—and the concepts related to it—manifest in different countries and language areas in Twitter discourse. To address this issue, this study explores misinformation and its related concepts: disinformation, fake news, and propaganda in Finnish language tweets. We utilized (1) word cloud clustering, (2) topic modeling, and (3) word count analysis and clustering to detect and analyze misinformation-related concepts and themes connected to those concepts in Finnish language Twitter discussions. Our results are two-fold: (1) those concerning the functional data analysis methods and (2) those about the themes connected in discourse to the misinformation-related concepts. We noticed that each utilized method individually has critical limitations, especially all the automated analysis methods processing for the Finnish language, yet when combined they bring value to the analysis. Moreover, we discovered that politics, both internal and external, are prominent in the Twitter discussions in connection with misinformation and its related concepts of disinformation, fake news, and propaganda.

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Section: Overview Of Key Conceptsmentioning

confidence: 99%

Section: Overview Of Key Conceptsmentioning

confidence: 99%

Section: Overview Of Key Conceptsmentioning

confidence: 99%

Section: Overview Of Key Conceptsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Text Analysis Methods for Misinformation–Related Research on Finnish Language Twitter

et al. 2021

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Nostalgia and negotiation: The electronic word‐of‐mouth and social well‐being of older consumers

Wilson‐Nash,

Pavlopoulou

2023

Psychology and Marketing

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As older people turn to the internet for consumption and social connection, it is imperative to understand how online consumption behaviors, such as generating and absorbing electronic word‐of‐mouth (eWOM), influence feelings of belonging. This study therefore explores how organic conversations around brands, products, and services influence older consumers' social well‐being. A 6‐month netnography was conducted in a social media platform geared toward older consumers where eWOM activity was created relating to books, household items, technology, furniture, financial services, clothing, and leisure activities. The findings reveal four types of eWOM—nostalgic, seeking reassurance/advice, providing reassurance/advice, and negotiation, which create experiences of social well‐being. This research contributes to the marketing literature by (1) exploring the implications of eWOM on consumer well‐being (2) investigating how the social value of eWOM interacts with social well‐being, and (3) developing pioneering knowledge of older consumers generating and absorbing eWOM.

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Recognizing Large‐Scale AIGC on Search Engine Websites Based on Knowledge Integration and Feature Pyramid Network

Wang,

Pan

et al. 2024

Proceedings of the Association for Information Science and Tech

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The proliferation of Artificial Intelligence Generated Content (AIGC) poses significant challenges to user experience and information accuracy, especially on search engine websites(Guo et al., 2023). The current solution is to identify AIGC by machine learning algorithms or publicly available AI detection tools, whereas, machine learning(Wang & Wang, 2022) algorithms degrade in accuracy as more data is available and tools such as GPTZero perform poorly in the task of AIGC detection on social media. In this paper, we propose an EPCNN model to identify AIGC on search engine websites, which maintains good performance in large‐scale samples. The ERNIE model integrates cross‐domain knowledge and improves language understanding and generalization. We use ERNIE to extract text features, then use a feature pyramid network to capture semantic information at different levels, and finally use an end‐to‐end structure to connect ERNIE and the feature pyramid network to construct the EPCNN. Experimental results show that our proposed algorithm has high accuracy and the ability to handle large‐scale data compared with machine learning algorithms and AI detection tools.

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Machine learning techniques and older adults processing of online information and misinformation: A covid 19 study

Cited by 105 publications

References 69 publications

Text Analysis Methods for Misinformation–Related Research on Finnish Language Twitter

Text Analysis Methods for Misinformation–Related Research on Finnish Language Twitter

Nostalgia and negotiation: The electronic word‐of‐mouth and social well‐being of older consumers

Recognizing Large‐Scale AIGC on Search Engine Websites Based on Knowledge Integration and Feature Pyramid Network

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