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Method to Utilize Personal Preferences to Social Media Feeds to Dynamically Increase the Accuracy and Flow of Information

IP.com Disclosure Number: IPCOM000246218D
Publication Date: 2016-May-17
Document File: 1 page(s) / 22K

Publishing Venue

The IP.com Prior Art Database

Abstract

Described is a method of reducing the time it takes for a user to scan through social media news feeds through the use of user defined preferred and non-preferred sources.

This text was extracted from a PDF file.
This is the abbreviated version, containing approximately 51% of the total text.

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Method to Utilize Personal Preferences to Social Media Feeds to Dynamically Increase the Accuracy and Flow of Information

Social media is an excellent, on-demand source of real-time information. It is an advantage that a user can monitor a number of different streams of information from a variety of sources, but culling through too much data can become cumbersome and unproductive. Some data sources are more trusted than others -- for example: news establishments could be preferred over individual user accounts for reporting some news events. However, a user may still find value in the content of the individual user accounts and not want to remove it from their news feed entirely. Proposed here is a method to give a user the ability to dynamically factor in their own 'believability' into their social media streams to reduce the time scrolling through articles of interest.

    A user monitors a social media application such as Twitter*, Facebook**, or LinkedIn***. When a major news event happens, the same news story can be reported by a number of similar sources. Instead of seeing the same article repeated in their news feed, the user can configure an option to display information more prominently from the user-preferred sources and de-emphasize or remove the articles from view that are generated from non-preferred sources. This example describes how it could be implemented with Twitter, but it could also be applied to other social media tools such as Facebook and LinkedIn.

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