2021
DOI: 10.1016/j.ipm.2021.102631
|View full text |Cite
|
Sign up to set email alerts
|

Misinformation detection using multitask learning with mutual learning for novelty detection and emotion recognition

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
2
1

Citation Types

0
16
0

Year Published

2021
2021
2024
2024

Publication Types

Select...
4
3
1

Relationship

0
8

Authors

Journals

citations
Cited by 44 publications
(22 citation statements)
references
References 30 publications
0
16
0
Order By: Relevance
“…Individuals online often feel like they are in a perpetual arms race to keep people's attention, with incentives ranging from self-validation to profit. Natural human tendencies to seek out exciting new information [48] are easily co-opted by highly emotional [49] and potentially misleading information [50]. Some individuals thrive on posting novel, but inflammatory content to convey a morally righteous self-image, elicit positive feedback, or solidify ingroup status [51][52][53].…”
Section: The Internet: Echo Chambers Anonymity and The Audiencementioning
confidence: 99%
See 1 more Smart Citation
“…Individuals online often feel like they are in a perpetual arms race to keep people's attention, with incentives ranging from self-validation to profit. Natural human tendencies to seek out exciting new information [48] are easily co-opted by highly emotional [49] and potentially misleading information [50]. Some individuals thrive on posting novel, but inflammatory content to convey a morally righteous self-image, elicit positive feedback, or solidify ingroup status [51][52][53].…”
Section: The Internet: Echo Chambers Anonymity and The Audiencementioning
confidence: 99%
“…Some individuals thrive on posting novel, but inflammatory content to convey a morally righteous self-image, elicit positive feedback, or solidify ingroup status [51][52][53]. Although this provocative content gains traction easily, it is likely to misrepresent, dehumanize, or contain false information about an outgroup, discouraging productive CIC [49,50,54].…”
Section: The Internet: Echo Chambers Anonymity and The Audiencementioning
confidence: 99%
“…Online audiences strongly in uence what content social media users decide to post, comment, or respond to. Individuals online often feel like they are in a perpetual arms race to keep people's attention, with incentives ranging from self-validation to pro t. Natural human tendencies to seek out exciting new information (42) are easily co-opted by highly emotional (43) and potentially misleading information (44). Some individuals thrive on posting novel, but in ammatory content to convey a morally righteous self-image, elicit positive feedback, or solidify ingroup status (45)(46)(47).…”
Section: Negative Forecasts and Avoidancementioning
confidence: 99%
“…Some individuals thrive on posting novel, but in ammatory content to convey a morally righteous self-image, elicit positive feedback, or solidify ingroup status (45)(46)(47). Although this provocative content gains traction easily, it is likely to misrepresent, dehumanize, or contain false information about an outgroup, discouraging productive CIC (43,44,48). Additionally, the presence of these "attitudinal ingroup" audiences may create a social pressure to espouse extreme opinions (49) to avoid "looking weak".…”
Section: Negative Forecasts and Avoidancementioning
confidence: 99%
“…With the onset of the COVID-19 outbreak, social media has served as an important tool for information generation, dissemination, and consumption, contributing to many high-quality COVID-19 related papers involving main aspects like infodemic, surveillance, and monitoring ( Shorten et al, 2021 ; Tsao et al, 2021 ). Infodemic papers are mainly about misinformation ( Agley & Xiao, 2021 ; Ahmed et al, 2020 ; Alaa et al, 2020 ), its detection ( Ayoub et al, 2021 ; Kouzy et al, 2020 ; Kumari et al, 2021 ), influence, exposure and spread ( Burel et al, 2020 , 2021 ; Hanyin et al, 2021 ; Obadimu et al, 2021 ; Tang, Fujimoto, et al, 2021 ). Surveillance and monitoring papers cover a wide range including assessing public sentiments ( Basiri et al, 2021 ; Blanco & Lourenço, 2022 ; Xuehua Han et al, 2020 ) like vaccine attitudes ( Aygun et al, 2021 ; Griffith et al, 2021 ; Lazarus et al, 2021 ), mental health ( Behl et al, 2021 ; Guntuku et al, 2020 ), and detecting or predicting COVID-19 trends ( Huang et al, 2021 ) and cases ( Shen et al, 2020 ).…”
Section: Introductionmentioning
confidence: 99%