1. A method, comprising:
identifying accounts associated with social networks;
collecting signals for the accounts;
associating contextual dimensions with the accounts;
identifying relationships between the accounts andor signals and the contextual dimensions;
generating analytics for the contextual dimensions based on the relationships;
identifying the signals containing messages for a conversation discussing a same topic;
identifying a first type of user participating in the conversation as influencers that have a plurality of followers;
identifying a second type of user participating in the conversation as advocates;
determining a first weighted score based at least in part on applying a first weight to a first portion of the messages generated by the influencers;
determining a second weighted score based at least in part on applying a second weight to a second portion of the messages generated by the advocates;
calculating a strength of the conversation based at least in part on the first weighted score and the second weighted score;
generating a score for the topic based at least in part on the strength of the conversation; and
displaying the score of the topic on a display.
2. The method of claim 1, further comprising identifying a relevance of the signals or accounts to the contextual dimensions and generating the analytics for the contextual dimensions based on the relevance.
3. The method of claim 1, further comprising:
periodically generating the analytics for the contextual dimensions for different time periods;
using a first set of the analytics for a first one of the time periods as a benchmark; and
comparing the first set of analytics to a second set of the analytics for a second one of the time periods to identify changes in the analytics.
4. The method of claim 1, comprising:
identifying a first set of the signals associated with a first one of the contextual dimensions;
generating a first set of analytics for the first one of the contextual dimensions from the first set of the signals;
identifying a second set of the signals associated with a second one of the contextual dimensions;
generating a second set of analytics for the second one of the contextual dimensions from the second set of the signals;
using the first set of analytics as a benchmark for comparing with the second set of analytics.
5. The method of claim 4, wherein the first one of the contextual dimensions comprises an industry and the second one of the contextual dimensions comprises a brand within the industry.
6. The method of claim 4, wherein the first one of the contextual dimensions comprises a brand and the second one of the contextual dimensions comprises a competitor brand.
7. The method of claim 4, wherein the first one of the contextual dimensions comprises a company and the second one of the contextual dimensions comprises a brand sold by the company.
8. The method of claim 4, wherein the first one of the contextual dimensions comprises a brand and the second one of the contextual dimensions comprises a geographic region for the brand.
9. The method of claim 1, further comprising:
identifying the accounts associated with the contextual dimensions as constituents of the contextual dimensions;
identifying the signals associated with the constituents; and
generating the analytics for the contextual dimensions based on the signals associated with the constituents of the contextual dimensions.
10. The method of claim 9, further comprising:
identifying the constituents generating positive messages about the associated contextual dimensions as advocates of the contextual dimensions;
identifying the constituents generating negative messages about the associated contextual dimensions as detractors of the contextual dimensions; and
identifying the constituents associated with the contextual dimensions and having a threshold number of followers as influencers of the contextual dimensions.
11. The method of claim 1, further comprising:
identifying the accounts associated with companies as company accounts;
identifying the accounts for employees of the companies as employee accounts; and
generating the analytics for the company accounts based on the signals associated with the employee accounts.
12. The method of claim 1, further comprising:
identifying social network sources associated with the contextual dimensions and having a threshold number of followers as influencers of the contextual dimensions;
adding the social network sources to the accounts; and
collecting signals from the social network sources added to the accounts.
13. The method of claim 1 further comprising:
calculating the strength of the conversation based at least in part on a sentiment of the messages for the conversation, wherein the sentiment includes positive, neutral, and negative reviews in the messages discussing the topic.
14. An analytic system, comprising:
a computing system including a processor configured to execute software instructions stored in memory, the software instructions comprising:
a collector module configured to collect signals from sources in a social network, wherein the sources are identified by accounts;
an enrichment module configured to identify:
relationships between the signals and the accounts;
the signals containing messages for a conversation discussing a same topic;
a first type of user participating in the conversation as influencers that have a plurality of followers; and
a second type of user participating in the conversation as advocates;
an analysis module configured to:
determine a first weighted score based at least in part on applying a first weight to a first portion of the messages generated by the influencers;
determine a second weighted score based at least in part on applying a second weight to a second portion of the messages generated by the advocates;
calculate a strength of the conversation based at least in part on the first weighted score and the second weighted score;
generate a score for the topic based at least in part on the strength of the conversation; and
a display module configured to display the score of the topic on a display device.
15. The analytic system of claim 14, further comprising multiple collector modules configured to collect the signals from different social networks and store the collected signals into time buckets, wherein some of the signals are collected by the collector modules polling the social networks and some of the signals are collected by the social networks streaming the signals to the collector modules.
16. The analytic system of claim 14, further comprising a normalize module configured to load data from the signals into a table and add identifiers to the signals identifying the accounts associated with the signals and identifying ecosystems for the accounts.
17. The analytic system of claim 14, wherein the enrichment module is further configured to identify contextual dimensions associated with the accounts.
18. The analytic system of claim 17, wherein the contextual dimensions comprise at least one of an industry associated with the accounts, a company associated with the accounts, brands or services associated with the accounts, andor geographic regions associated with the accounts.
19. The analytic system of claim 17, wherein the enrichment module is further configured to identify some of the accounts as constituent accounts for other related accounts.
20. The analytic system of claim 19 wherein the constituent accounts comprise at least one of:
advocate accounts generating positive messages for the related accounts;
employee accounts for employees of companies associated with the related accounts;
partner accounts for the accounts of business partners of the companies associated with the related accounts;
market accounts having some interactions with the related accounts; andor
influencer accounts having some interactions with the related accounts and having a threshold number of followers or subscribers.
21. The analytic system of claim 14, wherein the enrichment module is further configured to identify the signals associated with same conversations.
22. The analytic system of claim 21, wherein the enrichment module is further configured to:
identify the signals initiating the conversations;
identify types of messages used in the signals associated with the conversations;
identify contextual dimensions associated with the conversations; and
identifying a sentiment for the conversations.
23. The analytic system of claim 22, wherein the contextual dimensions comprise at least one of an industry, a company, a brand, andor a geographic region associated with the conversation.
24. The analytic system of claim 14, wherein the analysis module is further configured to:
identify a social network source generating some of the signals;
identify the social network source has having a large number of subscribers; and
direct the collector module to add the social network source to the accounts and start collecting signals from the social network source.
The claims below are in addition to those above.
All refrences to claim(s) which appear below refer to the numbering after this setence.
1. A method comprising the steps of:
a computer tracking, via one or more sensor devices connected to the computer, one or more gestures performed by a user, and in response,
the computer moving a cursor on a display device of the computer in a manner which corresponds to the one or more gestures; and
based, at least in part, on an analysis of the one or more gestures, the computer determining whether a subsequent performance of one or more of the one or more gestures are potentially injurious, and if so, the computer taking a preventative action.
2. The method of claim 1, wherein the step of the computer taking the preventative action comprises the step of the computer changing a manner in which cursor movement corresponds to subsequent gestures such that subsequent gestures must differ from the one or more potentially injurious gestures to accomplish a similar movement of the cursor.
3. The method of claim 1, wherein the step of the computer taking the preventative action comprises the step of the computer recommending to the user a pause in making gestures to control the cursor.
4. The method of claim 1, wherein the step of the computer determining whether the subsequent performance of one or more of the one or more gestures are potentially injurious comprises the step of:
the computer determining whether a speed of any of the one or more gestures is at or near a threshold level of potential injury, based on a history of the user performing gestures similar to any of the one or more gestures.
5. The method of claim 1, wherein the step of the computer determining whether the subsequent performance of one or more of the one or more gestures are potentially injurious comprises the step of:
the computer determining whether accuracy of any of the one or more gestures is at or near a threshold level of potential injury, based on a history of the user performing gestures similar to any of the one or more gestures.
6. The method of claim 1, wherein the step of the computer determining whether the subsequent performance of one or more of the one or more gestures are potentially injurious comprises the step of:
the computer determining whether the user is experiencing tremors at or near a threshold level of potential injury, based on a history of the user performing gestures similar to any of the one or more gestures.
7. The method of claim 1, wherein the step of the computer determining whether the subsequent performance of one or more of the one or more gestures are potentially injurious comprises the step of:
the computer determining whether a number of the one or more gestures is at or near a threshold number of gestures that can be performed before the user experiences pain or fatigue, based on a history of the user performing gestures similar to any of the one or more gestures.
8. The method of claim 1, wherein the step of the computer determining whether the subsequent performance of one or more of the one or more gestures are potentially injurious comprises the step of:
the computer determining whether the one or more gestures are performed for a duration at or near a threshold level of time that the one or more gestures can be performed within before experiencing pain or fatigue, based on a history of the user performing gestures similar to any of the one or more gestures.
9. The method of claim 1, further comprising the steps of:
the computer receiving an indication that the user is experiencing fatigue or pain, and in response, the computer determining a threshold level for potential injury for the one or more gestures; and
the computer using the threshold level to determine if subsequent repeated gestures, similar to any of the one or more gestures, are nearing a point of potential injury.
10. The method of claim 9, wherein the step of the computer receiving the indication that the user is experiencing fatigue or pain comprises the step of the computer receiving a notification from the user that the user is experiencing fatigue or pain.
11. The method of claim 9, wherein the step of the computer receiving the indication that the user is experiencing fatigue or pain comprises the computer determining one or more of:
whether any of the one or more gestures is below a predefined level of speed;
whether any of the one or more gestures is below a predefined level of accuracy; and
whether the tracking of the one or more gestures indicates that the user is experiencing tremors beyond a predefined level.
12. The method of claim 1, further comprising the step of:
prior to the step of the computer tracking the one or more gestures performed by the user, the computer receiving from the user at least two different manners in which cursor movement corresponds to gestures performed by the user.
13. The method of claim 12, wherein the at least two different manners in which cursor movement corresponds to gestures performed by the user are ranked by difficulty.
14. The method of claim 12, wherein the at least two different manners in which cursor movement corresponds to gestures performed by the user are ranked by one or more of: ease of use, length of safe use, and user preference.
15. The method of claim 12, wherein at least one of the at least two different manners in which cursor movement corresponds to gestures performed by the user enables rehabilitation of an injury to the user.
16. The method of claim 2, wherein the step of the computer changing a manner in which cursor movement corresponds to subsequent gestures comprises the computer selecting a manner in which cursor movement corresponds to gestures that has a lower difficulty ranking than a current manner in which cursor movement corresponds to gestures.
17. The method of claim 9, further comprising the step of:
the computer determining that a rate of accuracy for the one or more gestures is increased by a predefined level, and in response, the computer increasing the threshold level for potential injury.
18. The method of claim 9, further comprising the step of:
the computer determining that a rate of speed for the one or more gestures is increased by a predefined level, and in response, the computer increasing the threshold level for potential injury.
19. The method of claim 2, further comprising the step of:
the computer determining that a rate of accuracy or speed for the one or more gestures is increased; and
wherein the step of the computer changing a manner in which cursor movement corresponds to subsequent gestures comprises the computer selecting a manner in which cursor movement corresponds to gestures that has a higher difficulty ranking than a current manner in which cursor movement corresponds to gestures.
20. The method of claim 2, further comprising the step of:
the computer determining that the one or more gestures are performed for a specified duration without having received an indication of pain or fatigue; and
wherein the step of the computer changing a manner in which cursor movement corresponds to subsequent gestures comprises the computer selecting a manner in which cursor movement corresponds to gestures that has a higher difficulty ranking than a current manner in which cursor movement corresponds to gestures.
21. A computer program product comprising one or more computer-readable tangible storage devices and computer-readable program instructions which are stored on the one or more computer-readable tangible storage devices and when executed by one or more processors of the computer of claim 1 perform the method of claim 1.
22. A computer system comprising one or more processors, one or more computer-readable memories, one or more computer-readable tangible storage devices and program instructions which are stored on the one or more computer-readable tangible storage devices for execution by the one or more processors via the one or more memories and when executed by the one or more processors perform the method of claim 1.