Abstract: Participant inclusion determination can include generating a textual analysis of a draft of an electronic communication in response to a sender preparing the draft for conveyance over an electronic communications network. A word relevance score can be computed for each word of the draft based on the textual analysis. Probabilistically weighted distances between the sender, an initial recipient of the electronic communication, and more additional recipient candidates for additionally receiving the electronic communication can be determined. The probabilistically weighted distances can correspond to hierarchical distances within a hierarchical structure corresponding to an organization in which the sender, the initial recipient, and one or more additional recipient candidates are members.

Abstract: A computer implemented method processes log events. A computer system determines dependence and independence of training log events. The computer system clusters the training log events with determined dependence and independence to form clustered training log events. The computer system generates an event association model using the clustered training log events. The computer system clusters the log events using the event association model.

Abstract: Computer-implemented methods, computer program products, and computer systems for mitigating frequency loss may include one or more processors configured for receiving first audio data corresponding to unobstructed user utterances, receiving second audio data corresponding to first obstructed user utterances, generating a frequency loss (FL) model representing frequency loss between the first audio data and the second audio data, receiving third audio data corresponding to one or more second obstructed user utterances, processing the third audio data using the FL model to generate fourth audio data corresponding to a frequency loss mitigated version of the second obstructed user utterances, and transmitting the fourth audio data to a recipient computing device. The first obstructed user utterances are obstructed by a facemask and the one or more second obstructed user utterances is obstructed by the facemask. The FL model may be executed as an audio plugin in a web conferencing program.

Abstract: In an approach to collaborative discourse, responsive to receiving a collaborative discourse, a document corpora of the collaborative discourse is analyzed. A picture metadata is analyzed for each image in a graphic repository. A machine learning model is derived based on the analysis of the document corpora and the analysis of the picture metadata. Appropriate images are selected from the graphic repository based on the machine learning model, where the appropriate images closely align with the collaborative discourse.

Abstract: An embodiment for determining presentation content effectiveness using attraction modeling is provided. The embodiment may include receiving presentation content from a meeting host during on online collaborative meeting. The embodiment may also include capturing one or more actions of one or more users during a display of the presentation content to the one or more users. The embodiment may further include creating an audience attention model. The embodiment may also include in response to determining at least one user is distracted from the presentation content, modifying the display of the presentation content for each distracted user in accordance with one or more characteristics associated with each distracted user. The embodiment may further include categorizing the one or more users into one or more groups. The embodiment may also include providing visual feedback to the meeting host.

Abstract: Digital photograph filters and associated settings are recommended by an image classification model. A plurality of images and metadata associated with each of the plurality of images are received. Human interaction scores for each of the images are received. Training data is generated classifying the images using the associated metadata and human interaction scores. The training data is used to derive an image classification model for predicting a human interaction score for an image having an unknown human interaction score. At least one recommended image capture setting using the image classification model is determined in response to determining that a user is preparing to capture an image with a device. The recommended image capture setting is displayed on a display of the device.

Abstract: A method, a computer system, and a computer program product for managing content relevancy is provided. Embodiments of the present invention may include collecting and analyzing a plurality of data, wherein the plurality of data includes document data, document access data and user data. Embodiments of the present invention may include retrieving topic model content based on the plurality of data. Embodiments of the present invention may include building a machine learning (ML) model to determine one or more topics contained in the topic model content. Embodiments of the present invention may include generating a heatmap based on the user data. Embodiments of the present invention may include building a content relevancy model (CRM) based on the ML model and the heatmap. Embodiments of the present invention may include determining an action state for the document data. Embodiments of the present invention may include storing the CRM.

Abstract: A ticket relating to a software event is received in a software development system. One or more public forums on software development is searched for the software event. Two or more topics on the software event are identified from one or more conversations from the one or more public forums that regard the software event. Two or more causes of the software event are determined by analyzing interrelations of the two or more topics.

Abstract: Embodiments herein disclose computer-implemented methods, computer program products and computer systems including one or more processors configured for receiving user profile data corresponding to a plurality of user profiles each comprising profile attributes, determining profile attribute match scores for each of the profile attributes between each of the plurality of user profiles based on respective profile attribute pairs, and generating mentorship match data based on the profile attribute match scores, wherein the mentorship match data corresponds to a mentorship match between each pair of the plurality of user profiles.

Abstract: Dynamically evaluating effectiveness of real-time messages is provided. A scalable signal reconstruction analysis of real-time message content in a question-and-answer format posted in a social network is performed. A message effectiveness evaluator model is generated based on the scalable signal reconstruction analysis of the real-time message content in the question-and-answer format. An effectiveness score corresponding to the real-time message content in the question-and-answer format is generated using the message effectiveness evaluator model.

Abstract: Dynamic chat discourse rephrasing is provided. An analysis is performed of a real-time chat discourse conducted between a plurality of users connected via a network. A discourse comprehension model is generated based on the analysis of the real-time chat discourse. A set of text is rephrased in the real-time chat discourse to decrease user time to comprehend the set of text using the discourse comprehension model.

Abstract: A user is detected navigating to a webpage. A plurality of web elements of the webpage is identified. One or more web elements is determined to have a risk score that surpasses a risk threshold of being a dark pattern. The risk score is identified by analyzing each of the plurality of web elements. A mitigation action configured to counteract the dark pattern is executed in response to the one or more web elements having the risk score that surpasses the risk threshold. The mitigating action is executed substantially immediately upon the user navigating to the webpage.

Abstract: Systems and methods of the invention determine evasiveness of postings and manage chat sessions accordingly. In embodiments, a method includes accessing a real-time text-based discourse session comprised of multiple text-based posts published by participants, the posts including a question from an author and responses from at least one respondent; determining relationships between words in the text-based discourse session utilizing corpus linguistics analysis; determining a frequency of the responses of the at least one respondent over time; determining an evasiveness score for each of the responses based on natural language processing of the responses, wherein each of the evasiveness scores indicate a level of relevance of a response with respect to the question; determining rankings for each of the responses based on the determined relationships of words, the frequency of the responses, and the evasiveness scores; and determining a display order for the responses based on the rankings of the responses.

Abstract: A computer adjusts a set of presentation materials, comprising. The computer receives, from a source available to the computer, an initial set of presentation materials. The computer, in response to receiving the initial set of presentation materials, determines a reference presentation duration associated with initial set of presentation materials. The computer receives a target presentation duration from a target duration source available to the computer. The computer determines a presentation conversion value representing, at least in part, a ratio of the target presentation duration to the reference presentation duration. The computer applies a Machine Learning (ML) refactoring routine to revise the initial set of presentation materials in accordance, at least partially, with the presentation conversion value, thereby generating a refactored set of presentation materials having a revised conveyance duration substantially the same as the target duration.

Abstract: A method, computer system, and a computer program product for directed calendaring is provided. The present invention may include generating a topic model for each of a plurality of previously conducted meetings based on calendar data for a plurality of users. The present invention may include receiving a scheduled meeting, the scheduled meeting including an initial recipient list. The present invention may include generating a new topic model for the scheduled meeting. The present invention may include identifying a previous meeting with a highest similarity score between the topic model and the new topic model for each of the plurality of users. The present invention may include adjusting the initial recipient list of the scheduled meeting based on a transition matrix.

Abstract: A communication session between two or more computing devices is monitored. One or more share actions, that include one or more media documents, are identified in the communication session responsive to the media documents and based on the monitoring the communication session. Reaction data, that is related to the share actions, in the communication session is analyzed in response the share actions and based on the media documents. One or more relevant media documents are determined based on the analyzing the reaction data. The relevant media documents are analyzed based on the media documents. A first depiction rendered by a first of the devices of the communication session is modified based on the relevant media documents.

Abstract: Stepwise relationship cadence management can include generating a discourse cadence and confidence (DCC) measure based on a response message. The response message is made in replying to an originating message during a multi-party discourse over an electronic communication channel. The DCC measure indicates a likelihood of improving cadence and confidence with respect to an originator of the originating message and is based on a stepwise relational confidence model (SRCM) generated from an analysis of a plurality of prior multi-party discourses. Stepwise relationship cadence management can also include prompting a user to provide a follow-on message in response to determining that the response message made in replying to the originating message is not likely to improve cadence and confidence.

Abstract: In an approach to determining the effectiveness of a proposed solution, one or more computer processors monitor real-time communications. The one or more computer processors identify or more topics associated with the monitored real-time communications. The one or more computer processors feed the identified one or more topics and associated real-time communications into a solution efficacy model. The one or more computer processors generate based on one or more calculations by the solution efficacy model, an efficacy rating for the identified real-time communications. The one or more computer processors generate a prioritization of the identified real-time communications based on the generated efficacy rating.

Abstract: A method, computer system, and computer program product for petition creation through social analytics are provided. The embodiment may include generating a repository of user social data. The embodiment may also include extracting one or more social topics that are related to the user social data. The embodiment may further include generating a social supplication model using the repository and the extracted social topic. The embodiment may also include determining discussion issues within the social topic and a topic discussion distance between each user based on the generated social supplication model. The embodiment may further include generating a petition based on a determined discussion issue.

Abstract: An approach for directing a chat agent for an online discussion is provided. A processor retrieves message data from a messaging program. A processor identifies at least one topic present in the message data. A processor, responsive to a determination that the at least one topic is a primary topic, updates the chat agent with the primary topic. A processor modifies message data based on the determined primary topic.