Disclosed are systems and methods for improving interactions with and between computers in social media content generation and delivery and/or providing systems supported by or configured with personal computing devices, servers and/or platforms. The systems interact to identify and retrieve data within or across platforms, which can be used to improve the quality of data used in processing interactions between or among processors in such systems. The disclosed systems and methods provide systems and methods for automatically method for automatically summarizing social media content using a timeline comprising a set (or chain) of episodes and a summary of each episode. The disclosed systems and methods identify a number of episodes based on analysis of each social media content item of a corpus, identify a number of social content items to summarize each episode, and generate a timeline summarization of the corpus of social media content items.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method comprising: receiving, at a computing device, a request from a social media service user for information about an entity; identifying, via the computing device, a plurality of social media content items, the social media content item identification comprising searching a number of social media content item data stores for the plurality of social media content items containing information about the entity; determining, via the computing device and using each social media content item of the plurality, an episode timeline comprising a number of episodes, the episode timeline determination comprising modeling, using statistical machine modeling, content of the plurality of social media content items in combination with temporal information of the plurality of social media content items, the timeline determination further comprising assigning a number of social media content items of the plurality to each episode of the number; determining, via the computing device and for an episode, a ranking of the number of social media content items assigned to the episode, the rank determination comprising determining a set of features for each social media content item of the number and using statistical machine modeling to rank the social media content items of the number; automatically selecting, via the computing device and for the episode, a predetermined number of top-ranked social media content items from the number assigned to the episode; and automatically creating, via the computing device, a timeline summarization of the plurality of social media content items, the timeline summarization comprising a number of entries corresponding to the number of episodes of the episode timeline, an entry comprising the content of the predetermined number of top-ranked social media content items automatically selected for a corresponding episode of the number of episodes. 2. The method of claim 1, further comprising: communicating, via the computing device, the timeline summarization over an electronic communications network for display on a device of the user. 3. The method of claim 1, wherein the statistical machine modeling comprises a Bayesian nonparametric model that models the content using a multinomial distribution and models the temporal information using a Gamma distribution. 4. The method of claim 3, wherein the content of a social media content item comprises regular content modeled and hashtag content which are modeled separately using a two multinomial distributions. 5. The method of claim 3, further comprising: determining, via the computing device, a posterior distribution of the Bayesian nonparametric model using Gibbs Sampling. 6. The method of claim 3, the posterior distribution determination further comprising: generating, via the computing device, a time series using the temporal information of the plurality of social media content items; determining, via the computing device and the generated time series, a plurality of temporal bursts, each temporal burst of the plurality being an episode approximation; and initializing, via the computing device, the Gibbs Sampling using the determined plurality of temporal bursts. 7. The method of claim 1, the rank determination further comprising: determining, via the computing device and for the episode, the set of features for each social media content item, wherein the set of features comprising a number of temporal-based, content-based and popularity-based features. 8. The method of claim 7, the feature set determination further comprising: determining, via the computing device and for the episode, a local temporal peak of the episode using the temporal information of the number of social media content items assigned to the episode; and determining, via the computing device and for the episode, a temporal-based feature for a social media content item assigned to the episode, the temporal-based feature of the social media content item assigned to the episode being a difference between the temporal information of the social media content item and the determined local temporal peak of the episode. 9. The method of claim 7, the feature set determination further comprising: determining, via the computing device and for the episode, a number of term frequency vectors corresponding to the number of social media content items assigned to the episode, the term frequency vector determination comprising using frequency-inverse document frequency (TF-IDF) and terms used in a social media content item assigned to the episode to determine the social media content item's term frequency vector; determining, via the computing device and for the episode, a centroid vector of the episode using the number of term frequency vectors corresponding to the number of social media content items assigned to the episode; and determining, via the computing device and for the episode, a content-based feature for a social media content item assigned to the episode, the content-based feature of the social media content item assigned to the episode being a difference between the term frequency vector determined for the social media content item and the centroid vector determined for the episode. 10. The method of claim 7, the feature set determination further comprising: determining, via the computing device and for the episode, a content-based feature for a social media content item assigned to the episode, the content-based feature determination comprising detecting a language used in the content of the social media content item assigned to the episode. 11. The method of claim 7, the feature set determination further comprising: determining, via the computing device and for the episode, a content-based feature for a social media content item assigned to the episode, the content-based feature determination comprising determining a length of the content of the social media content item assigned to the episode. 12. The method of claim 7, the feature set determination further comprising: determining, via the computing device and for the episode, a popularity-based feature for a social media content item assigned to the episode, the popularity-based feature being based a normalization of a number of popularity measures. 13. The method of claim 12, the number of popularity measures comprising a number of replies to the social media content item, a number of reposts of the social media content item, a number of followers of an author of the social media content item, and a number of likes associated with the social media content item. 14. The method of claim 1, the automatic selection of a predetermined number of top-ranked social media content items from the number assigned to the episode, further comprising: automatically selecting, via the computing device, the predetermined number of top-ranked social media content items from the number assigned to the episode using a Gradient Boosted Decision Tree machine learning algorithm. 15. A non-transitory computer-readable storage medium tangibly encoded with computer-executable instructions, that when executed by a processor associated with a computing device, performs a method comprising: receiving a request from a social media service user for information about an entity; identifying a plurality of social media content items, the social media content item identification comprising searching a number of social media content item data stores for the plurality of social media content items containing information about the entity; determining, using each social media content item of the plurality, an episode timeline comprising a number of episodes, the episode timeline determination comprising modeling, using statistical machine modeling, content of the plurality of social media content items in combination with temporal information of the plurality of social media content items, the timeline determination further comprising assigning a number of social media content items of the plurality to each episode of the number; determining, for an episode, a ranking of the number of social media content items assigned to the episode, the rank determination comprising determining a set of features for each social media content item of the number and using statistical machine modeling to rank the social media content items of the number; automatically selecting, for the episode, a predetermined number of top-ranked social media content items from the number assigned to the episode; and automatically creating a timeline summarization of the plurality of social media content items, the timeline summarization comprising a number of entries corresponding to the number of episodes of the episode timeline, an entry comprising the content of the predetermined number of top-ranked social media content items automatically selected for a corresponding episode of the number of episodes. 16. The non-transitory computer-readable storage medium of claim 15, wherein the statistical machine modeling comprises a Bayesian nonparametric model that models the content using a multinomial distribution and models the temporal information using a Gamma distribution. 17. The non-transitory computer-readable storage medium of claim 16, wherein the content of a social media content item comprises regular content modeled and hashtag content which are modeled separately using a two multinomial distributions. 18. The non-transitory computer-readable storage medium of claim 16, further comprising: determining, via the computing device, a posterior distribution of the Bayesian nonparametric model using Gibbs Sampling. 19. The non-transitory computer-readable storage medium of claim 18, the posterior distribution determination further comprising: generating, via the computing device, a time series using the temporal information of the plurality of social media content items; determining, via the computing device and the generated time series, a plurality of temporal bursts, each temporal burst of the plurality being an episode approximation; and initializing, via the computing device, the Gibbs Sampling using the determined plurality of temporal bursts. 20. A computing device comprising: a processor; a non-transitory storage medium for tangibly storing thereon program logic for execution by the processor, the program logic comprising: receiving logic executed by the processor for receiving a request from a social media service user for information about an entity; identifying logic executed by the processor for identifying a plurality of social media content items, the social media content item identification comprising searching a number of social media content item data stores for the plurality of social media content items containing information about the entity; determining logic executed by the processor for determining, using each social media content item of the plurality, an episode timeline comprising a number of episodes, the episode timeline determination comprising modeling, using statistical machine modeling, content of the plurality of social media content items in combination with temporal information of the plurality of social media content items, the timeline determination further comprising assigning a number of social media content items of the plurality to each episode of the number; determining logic executed by the processor for determining, for an episode, a ranking of the number of social media content items assigned to the episode, the rank determination comprising determining a set of features for each social media content item of the number and using statistical machine modeling to rank the social media content items of the number; selecting logic executed by the processor for automatically selecting, for the episode, a predetermined number of top-ranked social media content items from the number assigned to the episode; and creating logic executed by the processor for automatically creating a timeline summarization of the plurality of social media content items, the timeline summarization comprising a number of entries corresponding to the number of episodes of the episode timeline, an entry comprising the content of the predetermined number of top-ranked social media content items automatically selected for a corresponding episode of the number of episodes.