Autopsy of a metaphor: The origins, use and blind spots of the ‘infodemic’

Simon, Felix M.; Camargo, Chico Q.

doi:10.1177/14614448211031908

Cited by 66 publications

(54 citation statements)

References 78 publications

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“…Our grasp of what an “infodemic” is and how it happens is still shallow and evolving. A possible reason for the mist around the term “infodemic” may reside in its very nature of an intuitive umbrella term that, however, includes many ramifications ranging from communication to epidemiology and that links to several open scientific debates such as that on misinformation spreading and its effects on society ( Simon and Camargo, 2021 ). To be more specific, the term “infodemic,” intended to mean an “epidemic of information,” was introduced by Rothkopf (2003) to define the amplification effect of the news about severe acute respiratory syndrome (SARS) due to information technologies.…”

Section: Main Textmentioning

confidence: 99%

Infodemics: A new challenge for public health

Briand

Cinelli

Nguyen

et al. 2021

Cell

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The COVID-19 information epidemic, or “infodemic,” demonstrates how unlimited access to information may confuse and influence behaviors during a health emergency. However, the study of infodemics is relatively new, and little is known about their relationship with epidemics management. Here, we discuss unresolved issues and propose research directions to enhance preparedness for future health crises.

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Section: Main Textmentioning

confidence: 99%

Infodemics: A new challenge for public health

Briand

Cinelli

Nguyen

et al. 2021

Cell

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“…The same tweet will create very different mental representations in each brain that reads it, and the public representations people leave behind them, in the form of digital traces, are only an imperfect proxy of their private mental representations. The virus metaphor, all too popular during the COVID-19 pandemic -think of the "infodemic" epithet -is misleading (Simon & Camargo, 2021). It is reminiscent of outdated models of communication (e.g.…”

Section: Misinformation Has a Strong Influence On People's Behaviormentioning

confidence: 99%

Misinformation on Misinformation: Conceptual and Methodological Challenges

Altay¹,

Berriche²,

Acerbi³

2021

Preprint

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Alarmist narratives about online misinformation continue to gain traction despite evidence that its prevalence and impact are overstated. Drawing on research questioning the use of big data in social science and reception studies, we identify six misconceptions about misinformation and examine the conceptual and methodological challenges they raise. The first three misconceptions concern the prevalence and circulation of misinformation. First, the internet is not rife with misinformation or news, but with memes and entertaining content. Second, scientists focus on social media because it is methodologically convenient, but misinformation is not just a social media problem. Third, falsehoods do not spread faster than the truth; how we define (mis)information influences our results and their practical implications. The second three misconceptions concern the impact and the reception of misinformation. First, people do not believe everything they see on the internet: sheer volume of engagement should not be conflated with belief. Second, the influence of misinformation on people’s behavior is overblown since it often ‘preaches to the choir’. Third, people are more likely to be uninformed than misinformed; surveys overestimate misperceptions and say little about the causal influence of misinformation. To appropriately understand and fight misinformation, future research needs to address these challenges.

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“…Similarly, DASentimental could be applied to track the change of explicit expression of depression, anxiety, and stress over history, quantified through the emotions of modern individuals. This would highlight changes in norms towards emotional expression and historical events (e.g., "pandemic"), thus complementing other recent approaches in cognitive network science [9,30,[59][60][61] and sentiment/emotional profiling [51,55,62,63] by bringing to the table a quantitative, automatic quantification of depression, anxiety, and stress in texts.…”

Section: Limitations and Future Researchmentioning

confidence: 85%

DASentimental: Detecting Depression, Anxiety, and Stress in Texts via Emotional Recall, Cognitive Networks, and Machine Learning

Fatima

Hills

et al. 2021

BDCC

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Most current affect scales and sentiment analysis on written text focus on quantifying valence/sentiment, the primary dimension of emotion. Distinguishing broader, more complex negative emotions of similar valence is key to evaluating mental health. We propose a semi-supervised machine learning model, DASentimental, to extract depression, anxiety, and stress from written text. We trained DASentimental to identify how N = 200 sequences of recalled emotional words correlate with recallers’ depression, anxiety, and stress from the Depression Anxiety Stress Scale (DASS-21). Using cognitive network science, we modeled every recall list as a bag-of-words (BOW) vector and as a walk over a network representation of semantic memory—in this case, free associations. This weights BOW entries according to their centrality (degree) in semantic memory and informs recalls using semantic network distances, thus embedding recalls in a cognitive representation. This embedding translated into state-of-the-art, cross-validated predictions for depression (R = 0.7), anxiety (R = 0.44), and stress (R = 0.52), equivalent to previous results employing additional human data. Powered by a multilayer perceptron neural network, DASentimental opens the door to probing the semantic organizations of emotional distress. We found that semantic distances between recalls (i.e., walk coverage), was key for estimating depression levels but redundant for anxiety and stress levels. Semantic distances from “fear” boosted anxiety predictions but were redundant when the “sad–happy” dyad was considered. We applied DASentimental to a clinical dataset of 142 suicide notes and found that the predicted depression and anxiety levels (high/low) corresponded to differences in valence and arousal as expected from a circumplex model of affect. We discuss key directions for future research enabled by artificial intelligence detecting stress, anxiety, and depression in texts.

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Autopsy of a metaphor: The origins, use and blind spots of the ‘infodemic’

Cited by 66 publications

References 78 publications

Infodemics: A new challenge for public health

Infodemics: A new challenge for public health

Misinformation on Misinformation: Conceptual and Methodological Challenges

DASentimental: Detecting Depression, Anxiety, and Stress in Texts via Emotional Recall, Cognitive Networks, and Machine Learning

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