17. A computer program product in accordance with claim 13 , further comprising program instructions to convert informative comments selected for presentation to a user's preferred language in text, audio, or video form.
13. A computer system for automatically augmenting online content with contextually relevant comments, the computer system comprising: one or more computer processors, one or more computer-readable storage media devices, and program instructions stored on one or more of the computer-readable storage media devices for execution by at least one of the one or more processors, the program instructions comprising: program instructions to receive from a network server that hosts an online content provider, online content from the online content provider; program instructions to receive from the online content provider, user-entered comments associated with the online content; program instructions to classify the comments a trained machine learning model as either chit-chat if the comment corresponds to training data characterized as meaningless, unhelpful, or unimportant, or informative if the comment corresponds to training data characterized as containing valuable information; for each comment classified as informative, program instructions to: determine a portion of the online content to which the comment is most relevant, wherein the program instructions further comprise program instructions to: convert the online content data to text; assign, by the computer, scores to segments of the text indicating a relevance of the comment to each segment, and identifying a segment with the highest score; and associate the comment with a position in the online content that corresponds to the determined portion of the online content; program instruction to select a subset of the informative comments for presentation based on a predefined criteria, wherein the predefined criteria comprises a selection based on one or more of: informative comments having a score exceeding a predefined threshold, recentness, randomness, a known or inferred profile of a user, a probabilistic basis according to a predefined distribution, or in real time, concurrently with user interaction with the online content; program instructions to convert the selected subset of the informative commands from text to a format suitable for presentation with the online content data; and program instructions to annotate the online content with the selected subset of informative comments at the positions in the online content associated with respective comments in the subset of informative comments.