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Identifying posted social contents that are potentially missing owner engagement Disclosure Number: IPCOM000246375D
Publication Date: 2016-Jun-02
Document File: 2 page(s) / 22K

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The Prior Art Database


People are contributing more and more contents in social applications today. Contributed content frequently gets reactions, missing such reaction can lead to missing important insights that you should be aware of, missing collaboration opportunities, etc. We propose an algorithm to identify social network entries you posted that have many reactions but lack owner engagement back with the reactors.

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Identifying posted social contents that are potentially missing owner engagement

Social applications become more and more popular today. Users contribute lot of content, which gets reactions such as likes, comments, shares from others. The more content user places the harder it is to follow others' comments and verify that he have read them and responded if needed back to the people who took time to engage with it. Missing such opportunities can lead to missing important insights that user should be aware of, missing collaboration and business opportunities, etc. To assist social network applications' users we propose to enrich such applications with a feature that recommends to users their contributed content which requires their engagement through commenting, liking, etc. This feature could be based on an algorithm to identify content one posted that have many reactions but lack owner engagement back with the reactors.Advantage of such feature is that it helps users focus on missed interactions with other users, save time, and reduce chance of missing opportunities or hurting others' feelings.

Currently systems support only manual inspection of posted content. Examples of existing implementations:

' Inside the tool: visual notifications in Facebook, LinkedIn, etc.

' Outside the tool: email notifications sent by tools according preferences defined by the

Active content contributors with an active audience have a hard time following their growing stream of events and content even if placed just few days ago. Notifications can easily go without notice even in emails or other communication channels. Already existing recommendations within social networking systems are focused on people to connect to or content to read but not on missing user engagement on specific content within the system. Being able to automatically identify and prioritize a specific subset of entries (in a defined time frame) one may need to address instead of going through them all and inquiring each, can be of great value to the social network user.

The identified entries could be exposed to users via an alerting system, part of an activity recommendation list or as an activity stream filtering option.

Our algorithm identifies a subset of posted entities a user posted in the social network that has comments from others on it but seems that the user did not respond back enough.

The computation algorithm should take into account:

' Level of reactions others did

' Level of user's reaction to others' contributions

' Whether the reaction's content requires a reaction from the user through NLP analysis

' Who is the person who wrote the response and his closeness to the user, like for example manager, peer...