Social Filtering: User-Centric Approach to Social Trend Prediction

Chepurna, Iuliia; Makrerhchi, Masoud

doi:10.1109/wi.2016.0115

Cited by 2 publications

(2 citation statements)

References 11 publications

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“…These factors can help them decide whether they want to connect with or follow the group. Social filtering, also collaborative filtering, is how a group organizes to allow people to find what could be useful or entertaining [ 43 - 45 ]. For example, if a tweet gets a lot of likes or retweets or a blog post is circulated across Twitter, Facebook, and numerous aggregators, this is interpreted as the judgment of the community.…”

Section: Discussionmentioning

confidence: 99%

Considerations for Health Researchers Using Social Media for Knowledge Translation: Multiple Case Study

et al. 2020

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Background Despite extensive literature describing the use of social media in health research, a gap exists around best practices in establishing, implementing, and evaluating an effective social media knowledge translation (KT) and exchange strategies. Objective This study aims to examine successes, challenges, and lessons learned from using social media within health research and to create practical considerations to guide other researchers. Methods The Knowledge Translation Platform of the Alberta Strategy for Patient-Oriented Research SUPPORT Unit formed a national working group involving platform staff, academics, and a parent representative with experience using social media for health research. We collected and analyzed 4 case studies that used a variety of social media platforms and evaluation methods. The case studies covered a spectrum of initiatives from participant recruitment and data collection to dissemination, engagement, and evaluation. Methods and findings from each case study as well as barriers and facilitators encountered were summarized. Through iterative discussions, we converged on recommendations and considerations for health researchers planning to use social media for KT. Results We provide recommendations for elements to consider when developing a social media KT strategy: (1) set a clear goal and identify a theory, framework, or model that aligns with the project goals and objectives; (2) understand the intended audience (use social network mapping to learn what platforms and social influences are available); (3) choose a platform or platforms that meet the needs of the intended audience and align well with the research team’s capabilities (can you tap into an existing network, and what mode of communication does it support?); (4) tailor messages to meet user needs and platform requirements (eg, plain language and word restrictions); (5) consider timing, frequency, and duration of messaging as well as the nature of interactions (ie, social filtering and negotiated awareness); (6) ensure adequate resources and personnel are available (eg, content creators, project coordinators, communications experts, and audience stakeholder or patient advocate); (7) develop an evaluation plan a priori driven by goals and types of data available (ie, quantitative and qualitative); and (8) consider ethical approvals needed (driven by evaluation and type of data collection). Conclusions In the absence of a comprehensive framework to guide health researchers using social media for KT, we provide several key considerations. Future research will help validate the proposed components and create a body of evidence around best practices for using and evaluating social media as part of a KT strategy

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Section: Discussionmentioning

confidence: 99%

Considerations for Health Researchers Using Social Media for Knowledge Translation: Multiple Case Study

et al. 2020

View full text Add to dashboard Cite

show abstract

“…These factors can help them decide whether they want to connect with or follow the group. Social filtering, also collaborative filtering, is how a group organizes to allow people to find what could be useful or entertaining [43][44][45]. For example, if a tweet gets a lot of likes or retweets or a blog post is circulated across Twitter, Facebook, and numerous aggregators, this is interpreted as the judgment of the community.…”

Section: How To Understand and Engage Web Audiences?mentioning

confidence: 99%

Considerations for Health Researchers Using Social Media for Knowledge Translation: Multiple Case Study (Preprint)

Elliott¹,

Dyson²,

Wilkes³

et al. 2019

Preprint

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BACKGROUND Despite extensive literature describing the use of social media in health research, a gap exists around best practices in establishing, implementing, and evaluating an effective social media knowledge translation (KT) and exchange strategies. OBJECTIVE This study aims to examine successes, challenges, and lessons learned from using social media within health research and to create practical considerations to guide other researchers. METHODS The Knowledge Translation Platform of the Alberta Strategy for Patient-Oriented Research SUPPORT Unit formed a national working group involving platform staff, academics, and a parent representative with experience using social media for health research. We collected and analyzed 4 case studies that used a variety of social media platforms and evaluation methods. The case studies covered a spectrum of initiatives from participant recruitment and data collection to dissemination, engagement, and evaluation. Methods and findings from each case study as well as barriers and facilitators encountered were summarized. Through iterative discussions, we converged on recommendations and considerations for health researchers planning to use social media for KT. RESULTS We provide recommendations for elements to consider when developing a social media KT strategy: (1) set a clear goal and identify a theory, framework, or model that aligns with the project goals and objectives; (2) understand the intended audience (use social network mapping to learn what platforms and social influences are available); (3) choose a platform or platforms that meet the needs of the intended audience and align well with the research team’s capabilities (can you tap into an existing network, and what mode of communication does it support?); (4) tailor messages to meet user needs and platform requirements (eg, plain language and word restrictions); (5) consider timing, frequency, and duration of messaging as well as the nature of interactions (ie, social filtering and negotiated awareness); (6) ensure adequate resources and personnel are available (eg, content creators, project coordinators, communications experts, and audience stakeholder or patient advocate); (7) develop an evaluation plan a priori driven by goals and types of data available (ie, quantitative and qualitative); and (8) consider ethical approvals needed (driven by evaluation and type of data collection). CONCLUSIONS In the absence of a comprehensive framework to guide health researchers using social media for KT, we provide several key considerations. Future research will help validate the proposed components and create a body of evidence around best practices for using and evaluating social media as part of a KT strategy

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Social Filtering: User-Centric Approach to Social Trend Prediction

Cited by 2 publications

References 11 publications

Considerations for Health Researchers Using Social Media for Knowledge Translation: Multiple Case Study

Considerations for Health Researchers Using Social Media for Knowledge Translation: Multiple Case Study

Considerations for Health Researchers Using Social Media for Knowledge Translation: Multiple Case Study (Preprint)

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