Innovative Routes for Enhancing Adolescent Marijuana Treatment: Interplay of Peer Influence Across Social Media and Geolocation

Chung, Tammy; Pelechrinis, Kostantinos; Faloutsos, Michalis; Hylek, Lindsay; Suffoletto, Brian; Ewing, Sarah W. Feldstein

doi:10.1007/s40429-016-0095-x

Cited by 3 publications

(3 citation statements)

References 78 publications

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“…This includes the correlation between geolocation of social media connections and various social determinants (eg, race, sex, and socioeconomic status) [ 51 ], and a study [ 54 ] determining optimal methods of geolocation on social media. Two additional studies discussed the sociological consequences of geolocation in the context of social media, namely, the detection and reduction of youth cannabis consumption [ 52 ] and the applications of geolocation to urban planning [ 53 ].…”

Section: Discussionmentioning

confidence: 99%

The Plebeian Algorithm: A Democratic Approach to Censorship and Moderation

Fedoruk¹,

Nelson²,

Frost³

et al. 2021

JMIR Form Res

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Background The infodemic created by the COVID-19 pandemic has created several societal issues, including a rise in distrust between the public and health experts, and even a refusal of some to accept vaccination; some sources suggest that 1 in 4 Americans will refuse the vaccine. This social concern can be traced to the level of digitization today, particularly in the form of social media. Objective The goal of the research is to determine an optimal social media algorithm, one which is able to reduce the number of cases of misinformation and which also ensures that certain individual freedoms (eg, the freedom of expression) are maintained. After performing the analysis described herein, an algorithm was abstracted. The discovery of a set of abstract aspects of an optimal social media algorithm was the purpose of the study. Methods As social media was the most significant contributing factor to the spread of misinformation, the team decided to examine infodemiology across various text-based platforms (Twitter, 4chan, Reddit, Parler, Facebook, and YouTube). This was done by using sentiment analysis to compare general posts with key terms flagged as misinformation (all of which concern COVID-19) to determine their verity. In gathering the data sets, both application programming interfaces (installed using Python’s pip) and pre-existing data compiled by standard scientific third parties were used. Results The sentiment can be described using bimodal distributions for each platform, with a positive and negative peak, as well as a skewness. It was found that in some cases, misinforming posts can have up to 92.5% more negative sentiment skew compared to accurate posts. Conclusions From this, the novel Plebeian Algorithm is proposed, which uses sentiment analysis and post popularity as metrics to flag a post as misinformation. This algorithm diverges from that of the status quo, as the Plebeian Algorithm uses a democratic process to detect and remove misinformation. A method was constructed in which content deemed as misinformation to be removed from the platform is determined by a randomly selected jury of anonymous users. This not only prevents these types of infodemics but also guarantees a more democratic way of using social media that is beneficial for repairing social trust and encouraging the public’s evidence-informed decision-making.

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Section: Discussionmentioning

confidence: 99%

The Plebeian Algorithm: A Democratic Approach to Censorship and Moderation

Fedoruk¹,

Nelson²,

Frost³

et al. 2021

JMIR Form Res

View full text Add to dashboard Cite

show abstract

“…Myriad studies have been conducted surrounding infodemics [51][52][53]. This includes the correlation between geolocation of social media connections and various social determinants (eg, race, sex, and socioeconomic status) [51], and a study [54] JMIR Form Res 2021 | vol.…”

Section: Geolocationmentioning

confidence: 99%

“…RenderX determining optimal methods of geolocation on social media. Two additional studies discussed the sociological consequences of geolocation in the context of social media, namely, the detection and reduction of youth cannabis consumption [52] and the applications of geolocation to urban planning [53].…”

Section: Xsl • Fomentioning

confidence: 99%

The Plebeian Algorithm: A Democratic Approach to Censorship and Moderation (Preprint)

Fedoruk¹,

Nelson²,

Frost³

et al. 2021

Preprint

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UNSTRUCTURED The infodemic created by the COVID-19 pandemic has created several societal issues, including a rise in distrust between the public and health experts and even a refusal of some to accept vaccination; some sources suggest that 1 in 4 Americans will refuse the vaccine. This social concern can be traced to the level of digitization today -- particularly in the form of social media. As social media was the most significant contributing factor to the spread of misinformation, the team decided to examine infodemiology across various text-based platforms (Twitter, 4chan, Reddit, Parler, Facebook, and YouTube). This was done by utilizing a sentiment analysis to compare general posts with key terms flagged as misinformation (all of which concern COVID-19) to determine their verity. In gathering the datasets, both APIs and also pre-existing data compiled by standard scientific third parties were used. It was found that in some cases, misinforming posts can have up to 92.5% more negative sentiment skew compared to accurate posts. From this, the novel Plebeian Algorithm is proposed, which utilizes sentiment analysis and post popularity as metrics to flag a post as misinformation. This algorithm diverges from that of the status quo, as the Plebeian Algorithm uses a democratic process to detect and remove misinformation. A method was constructed in which content deemed misinformation to be removed from the platform is determined by a randomly selected jury of anonymous users. This not only prevents these types of infodemics, but also guarantees a more democratic way of using social media that is beneficial for repairing social trust and encouraging the public's evidence-informed decision making.

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