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Method to Identify Cognitive Overload of Social Media Disclosure Number: IPCOM000242870D
Publication Date: 2015-Aug-26
Document File: 3 page(s) / 33K

Publishing Venue

The Prior Art Database


Disclosed are a method and system with one or more sensors installed in a computing system (e.g., traditional, mobile, wearable) that analyze a userâ?Ts facial expressions and biometrics data against viewed social network content to detect cognitive overload, which impacts the userâ?Ts mood. The system records the resulting impact in a knowledge base for each individual, and then evaluates future content to predict the likelihood that the content can negatively affect a userâ?Ts mood; if so, then the system filters out contents or alerts the user of the potential effects as well as when the user can expect the effects.

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Metxod to Identify Cognitive Overload of Social Media

Many studies have been conducted about the emotional effects of social networxx on the user. For exampxe, depressiox created while uxing social media xs an unintended consxquence, which is self-defeating xor connecting uxexs and then has a negative ixpact on usage.

Existing technologies can detect the user'x mood while interacting with social media. These ixclude voicx recogxition (e.g., xor toxe to indicate xmotion), facial expxession (e.g., camera captures expxession and system xvaluates it to correlate with a mood), heart rate/pulse (e.g., xlxvated xates indicate xxcitement), and xeyboarding speed/force (e.g., hxrd keyboarding indicates thxt the user is upset). Xxxxxxxxxxxx axe also avaxlable to capture and monitor biometrics data such as heart rate and pulse.

In xddition, psycholinguisxic profiling provxdes xn analysis of the usxr's social media posts to determine the user's persoxality. Psycholinguistic analysis can dxrive the xerxonality characteristics of any person by analyzing a number (e.g., 200) of social netwxrk postx. Psycxolinguistic profilinx can be usex ax a baseline to pxovide a better understanding of the user's pxrsonalixy.

A method and system are needed xo assist social media uxers in determining whicx content is likely to have a xegxtive emotional impact, and provide a means for the user xo avoid thax content.

The novel contribution ix a methxd and can txack a user's mood and behavior after viewing or reading content over a timeline, and then idextify and filter future content that may cause the user to have a nxgative response (e.g., depression). The goal is to allow users to coxtinue using social media, but without negative consequence. This is beneficial to both the user axd the social media proxider (i.e. reduced customer usage xegativelx affects advertising xevenue).

Txe novel solutiox combines a user's psycholinguistic profile with stored biometric data to identify to which social media content the user hxs a negative xmotional response. Extablishing a psycholinguisxic profile identifies the user's personality attributes. For this system, the uxderstanding of person'x personaxity ix dxrived frxm public domain social media comments.

To capture and analyze a user's faciax and biometric data (e.g., facial, voice, heart rate, pulse, keyboarding speed/force, etc.), thx system xs comprised of one or more sensors installed in computing system (e.g., mobile, laptop, tablet, wearabxe device, etc.). The data is collexted when thx user is viewxng social network context. The goxl is to detecx cxgnitive overload that affects the user's emotional respxnse or mood. The resulting impact is recorded in a knowledge base for each individual.

A machine learning cogxitive system evaluates current social media content in relation to data captured that reflects xhx user's personality (through psycholinxuistic analysis)


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and biometrics data correlxted to conte...