Abstract: A computer-implemented method includes receiving, by a first computing device, an identification of a video and an identification of a distribution channel of the video. The method includes retrieving, by the first computing device, from a video sharing network, metadata associated with a video. The method includes retrieving, by the first computing device, from a second computing device in a second network, data having at least one characteristic in common with the metadata. The method includes generating, by the first computing device, a profile of the video based on the retrieved data and the metadata. The method includes generating, by the first computing device, a profile of the distribution channel based on the retrieved data and metadata. The first computing device generates a recommendation for a method to increase a level of distribution of the video. The first computing device provides, to a user, the recommendation.

Abstract: A computer-implemented method includes receiving, by a first computing device, an identification of a video and an identification of a distribution channel of the video. The method includes retrieving, by the first computing device, from a video sharing network, metadata associated with a video. The method includes retrieving, by the first computing device, from a second computing device in a second network, data having at least one characteristic in common with the metadata. The method includes generating, by the first computing device, a profile of the video based on the retrieved data and the metadata. The method includes generating, by the first computing device, a profile of the distribution channel based on the retrieved data and metadata. The first computing device generates a recommendation for a method to increase a level of distribution of the video. The first computing device provides, to a user, the recommendation.

Abstract: A computer-implemented method includes receiving, by a first computing device, an identification of a video and an identification of a distribution channel of the video. The method includes retrieving, by the first computing device, from a video sharing network, metadata associated with a video. The method includes retrieving, by the first computing device, from a second computing device in a second network, data having at least one characteristic in common with the metadata. The method includes generating, by the first computing device, a profile of the video based on the retrieved data and the metadata. The method includes generating, by the first computing device, a profile of the distribution channel based on the retrieved data and metadata. The first computing device generates a recommendation for a method to increase a level of distribution of the video. The first computing device provides, to a user, the recommendation.

Abstract: A method for increasing a level of distribution of a media file includes receiving an identification of metadata of a media file, the metadata including at least one keyword. The method includes directing a semantic engine to map the at least one keyword to a plurality of candidate target segments, each of the plurality of candidate target segments associated with one of a plurality of social network platforms. The method includes providing access to an enumeration of the mapped plurality of candidate target segments. The method includes receiving an identification of a subset of the plurality of candidate target segments. The method includes directing the semantic engine to strengthen an association between each of the identified subset and the at least one keyword. The method includes generating a recommendation for a method to increase a distribution level of the media file based upon at least one of the identified subset.

Abstract: A computer-implemented method includes receiving, by a first computing device, an identification of a video and an identification of a distribution channel of the video. The method includes retrieving, by the first computing device, from a video sharing network, metadata associated with a video. The method includes retrieving, by the first computing device, from a second computing device in a second network, data having at least one characteristic in common with the metadata. The method includes generating, by the first computing device, a profile of the video based on the retrieved data and the metadata. The method includes generating, by the first computing device, a profile of the distribution channel based on the retrieved data and metadata. The first computing device generates a recommendation for a method to increase a level of distribution of the video. The first computing device provides, to a user, the recommendation.

Abstract: A computer-implemented method includes receiving, by a first computing device, an identification of a video and an identification of a distribution channel of the video. The method includes retrieving, by the first computing device, from a video sharing network, metadata associated with a video. The method includes retrieving, by the first computing device, from a second computing device in a second network, data having at least one characteristic in common with the metadata. The method includes generating, by the first computing device, a profile of the video based on the retrieved data and the metadata. The method includes generating, by the first computing device, a profile of the distribution channel based on the retrieved data and metadata. The first computing device generates a recommendation for a method to increase a level of distribution of the video. The first computing device provides, to a user, the recommendation.

Abstract: A computer-implemented method includes receiving, by a first computing device, an identification of a video and an identification of a distribution channel of the video. The method includes retrieving, by the first computing device, from a video sharing network, metadata associated with a video. The method includes retrieving, by the first computing device, from a second computing device in a second network, data having at least one characteristic in common with the metadata. The method includes generating, by the first computing device, a profile of the video based on the retrieved data and the metadata. The method includes generating, by the first computing device, a profile of the distribution channel based on the retrieved data and metadata. The first computing device generates a recommendation for a method to increase a level of distribution of the video. The first computing device provides, to a user, the recommendation.

Abstract: Methods, apparatuses, and computer-readable media for generating a social network graph to model one or more social networks of related authors of online social media and their corresponding posts of online social media conversations relevant to subject matter of interest in a category. Embodiments are configured to harvest and aggregate posts of online social media conversations from one or more online social media sources; to perform content scraping on the posts of online social media conversations to obtain raw data that includes user-profile information of a social media author corresponding to each individual post of online social media conversations; and performing social network analysis processing on the raw data to obtain the social network graph.

Abstract: Methods, apparatuses, and computer-readable media for generating a website network graph to model one or more networks of websites relevant to subject matter of interest in a category, wherein generating the website network graph includes performing one or more searches relating to the subject matter of interest in a search engine API using one or more relevant keywords in combination with the subject matter of interest, extracting search results from the one or more searches, and identifying online social media websites with content most relevant to the subject matter of interest based on the website network graph.

Abstract: Methods, apparatuses, and computer-readable media for generating a website network graph to model one or more networks of websites relevant to subject matter of interest in a category, wherein generating the website network graph includes performing one or more searches relating to the subject matter of interest in a search engine API using one or more relevant keywords in combination with the subject matter of interest, extracting search results from the one or more searches, and identifying online social media websites with content most relevant to the subject matter of interest based on the website network graph.

Abstract: Methods, apparatuses, and computer-readable media for generating a website network graph to model one or more networks of websites relevant to subject matter of interest in a category, wherein generating the website network graph includes performing one or more searches relating to the subject matter of interest in a search engine API using one or more relevant keywords in combination with the subject matter of interest, extracting search results from the one or more searches, and identifying online social media websites with content most relevant to the subject matter of interest based on the website network graph.

Abstract: Methods, apparatuses, and computer-readable media for generating a social network graph to model one or more social networks of related authors of online social media and their corresponding posts of online social media conversations relevant to subject matter of interest in a category. Embodiments are configured to harvest and aggregate posts of online social media conversations from one or more online social media sources; to perform content scraping on the posts of online social media conversations to obtain raw data that includes user-profile information of a social media author corresponding to each individual post of online social media conversations; and performing social network analysis processing on the raw data to obtain the social network graph.

Abstract: Methods, apparatuses, and computer-readable media for displaying one or more visualizations of quantitative measurements of the sentiment expressed among online social media participants concerning subject matter of interest in a category. Embodiments are configured for displaying how the sentiment among the online social media participants trends over time and how the sentiment trends with respect to social media source or groups of sources. Embodiments display the actionable information in a graphical interface in an intuitive and user-friendly manner at different levels of granularity that enables the quantified online social media content to be grouped and filtered in a variety of default and/or customizable ways.

Abstract: A social media analytics platform including methods, apparatuses and computer-readable media for providing analytic measurements of online social media content by harvesting and aggregating unstructured qualitative online social media conversations relevant to subject matter of interest in a category from one or more online social media sources, quantifying the aggregated online social media conversations, and providing actionable information based on the quantified aggregated online social media conversations, the actionable information including sentiment expressed among online social media participants concerning subject matter of interest in the category.