The Current State of Online Social Networking for the Health Community

Ohashi, Daniel; Cohen, Robin; Fu, Xin

doi:10.1145/3079452.3079462

Cited by 4 publications

(3 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…(Quite interestingly this was as well a period of time when the coronavirus emerged as a topic of concern. Healthcare misinformation is, in general, a topic where some have argued that detecting the posts is especially important, due to the consequences that users may suffer if they misinterpret the advice [14]. ) We are hypothesizing that each of several very popular social media environments today contains posts with misleading information.…”

Section: Methods: Assembling Social Network Misinformationmentioning

confidence: 99%

“…We feel that there is a preponderance of searches online precisely because of this particular concern, for a user. Misinformation easily pervades due to the nature of certain social networks to be populated with personal anecdotes [14], with non-experts offering medical advice, with questionable fact-checkers being cited (e.g., WebMD) and with the tendency to draw our sensationalized news stories as part of the conversation (where again this information may be coming from less reputable sources).…”

Section: Healthcare Misinformationmentioning

confidence: 99%

See 1 more Smart Citation

Addressing Misinformation in Online Social Networks: Diverse Platforms and the Potential of Multiagent Trust Modeling

et al. 2020

Self Cite

View full text Add to dashboard Cite

In this paper, we explore how various social networking platforms currently support the spread of misinformation. We then examine the potential of a few specific multiagent trust modeling algorithms from artificial intelligence, towards detecting that misinformation. Our investigation reveals that specific requirements of each environment may require distinct solutions for the processing. This then leads to a higher-level proposal for the actions to be taken in order to judge trustworthiness. Our final reflection concerns what information should be provided to users, once there are suspected misleading posts. Our aim is to enlighten both the organizations that host social networking and the users of those platforms, and to promote steps forward for more pro-social behaviour in these environments. As a look to the future and the growing need to address this vital topic, we reflect as well on two related topics of possible interest: the case of older adult users and the potential to track misinformation through dedicated data science studies, of particular use for healthcare.

show abstract

Section: Methods: Assembling Social Network Misinformationmentioning

confidence: 99%

Section: Healthcare Misinformationmentioning

confidence: 99%

Addressing Misinformation in Online Social Networks: Diverse Platforms and the Potential of Multiagent Trust Modeling

et al. 2020

Self Cite

View full text Add to dashboard Cite

show abstract

“…Our concern with trustworthiness of content relates well to companion efforts devoted to detect digital misinformation [7,21,47]. There is a spectrum of possible outcomes when messages which are of questionable quality are shown to users, including special attention in contexts such as healthcare where the consequences may be more troubling [32]. Note that there will still be various options for actions to take, once trust modeling has provided some insights into messages of concern.…”

Section: Toward Improved Handling Of Misinformationmentioning

confidence: 99%

Personalized multi-faceted trust modeling to determine trust links in social media and its potential for misinformation management

Parmentier

Cohen

et al. 2022

Int J Data Sci Anal

Self Cite

View full text Add to dashboard Cite

In this paper, we present an approach for predicting trust links between peers in social media, one that is grounded in the artificial intelligence area of multiagent trust modeling. In particular, we propose a data-driven multi-faceted trust modeling which incorporates many distinct features for a comprehensive analysis. We focus on demonstrating how clustering of similar users enables a critical new functionality: supporting more personalized, and thus more accurate predictions for users. Illustrated in a trust-aware item recommendation task, we evaluate the proposed framework in the context of a large Yelp data set. We then discuss how improving the detection of trusted relationships in social media can assist in supporting online users in their battle against the spread of misinformation and rumors, within a social networking environment which has recently exploded in popularity. We conclude with a reflection on a particularly vulnerable user base, older adults, in order to illustrate the value of reasoning about groups of users, looking to some future directions for integrating known preferences with insights gained through data analysis.

show abstract