Abstract: A computer-implemented method for filtering search engine results for a user is provided. The method includes maintaining a filtration layer that is opted into by a search engine and a client device. The method further includes building a user search interaction model, operatively coupled to the filtration layer, based on a user's profile and historic search results by performing a topic analysis on a user's interactions with the historic search results and selecting a subset of relevant topics based on respective amounts of user interaction. The user interaction includes interactions on a plurality of different devices. The method also includes filtering search results produced for a particular user search query on the client device using the user search interaction model and the filtration layer.

Abstract: Chat discourse convolution can include performing a lexicographic analysis of the content of a chat message of a chat discourse and storing a result of the lexicographic analysis in a database. The convolution can further include generating a hash of the content of the chat message and generating a leximarker. The leximarker can encapsulate a reference to the stored lexicographic analysis as well as the hash of the content of the chat message.

Abstract: Methods, systems, and computer readable media are provided for identifying photos such as selfies in which the individuals in the photo are in imminent danger. One or more profiles are generated, each profile associated with a corresponding first user of a networked system and pertaining to problematic conditions for image capture. An image of a second user is captured via an image capture device. The captured image of the second user is uploaded to the networked system. The captured image of the second user is compared to the generated one or more profiles to determine a score value for the captured image of the second user. A score value may be generated based upon a combination of scores from one or more of geolocation information, metadata analysis, and image processing and machine learning. The upload of the captured image of the second user is rejected in response to the score value satisfying a threshold indicating a presence of problematic conditions during capturing of the image of the second user.

Abstract: In enhancing real-time message verbosity, a computing device analyzes a prior message corpus for a sender over multiple vectors. The computing device derives the prior message model from results of the analysis of the prior message corpus. When the computing device receives new message text composed at the sender, the computing device applies a prior message model to the new message text. The prior message model representing behavior of the sender in prior message discourses with different communication device types and different message types. The computing device identifies a set of candidate texts based on the prior message model and inserts the set of candidate texts into the new message text to form modified message text. The computing device validates a readability of the modified message text and sends the modified message text to the sender device. The sender device then sends the modified message text to a recipient device.

Abstract: Chat discourse convolution can include performing a lexicographic analysis of the content of a chat message of a chat discourse and storing a result of the lexicographic analysis in a database. The convolution can further include generating a hash of the content of the chat message and generating a leximarker. The leximarker can encapsulate a reference to the stored lexicographic analysis as well as the hash of the content of the chat message.

Abstract: In enhancing real-time message verbosity, a computing device analyzes a prior message corpus for a sender over multiple vectors. The computing device derives the prior message model from results of the analysis of the prior message corpus. When the computing device receives new message text composed at the sender, the computing device applies a prior message model to the new message text. The prior message model representing behavior of the sender in prior message discourses with different communication device types and different message types. The computing device identifies a set of candidate texts based on the prior message model and inserts the set of candidate texts into the new message text to form modified message text. The computing device validates a readability of the modified message text and sends the modified message text to the sender device. The sender device then sends the modified message text to a recipient device.

Abstract: Methods, systems, and computer readable media are provided for identifying photos such as selfies in which the individuals in the photo are in imminent danger. One or more profiles are generated, each profile associated with a corresponding first user of a networked system and pertaining to problematic conditions for image capture. An image of a second user is captured via an image capture device. The captured image of the second user is uploaded to the networked system. The captured image of the second user is compared to the generated one or more profiles to determine a score value for the captured image of the second user. A score value may be generated based upon a combination of scores from one or more of geolocation information, metadata analysis, and image processing and machine learning. The upload of the captured image of the second user is rejected in response to the score value satisfying a threshold indicating a presence of problematic conditions during capturing of the image of the second user.