Abstract: This disclosure generally covers systems and methods that schedule posts of one or more images, videos, or other digital content items through an account of a social networking system in a way that optimizes user engagement. In particular, certain embodiments of the disclosed systems and methods determine when to post digital content items by calculating data-driven user engagement scores and updating those scores with data tracked by the social networking system.

Abstract: Technology is disclosed for building correction models that correct natural language snippets. Correction models can include rules comprising pairs of word sequences identified from viable correction snippet pairs, where a first sequence of words in the pair should be replaced with a second sequence of words in the pair. Viable correction snippet pairs can be identified from among pairs of language snippets, such as a post to a social media website and a subsequent update to that post. Viable corrections can be the snippet pairs that both have no more unaligned words than a word alignment threshold and have no aligned word pair with a character edit difference above an edit distance threshold. In some implementations, word alignments can be found by aligning all the characters between a pair of language snippets, and identifying aligned words as those that have at least one aligned letter in common.

Abstract: Technology is disclosed for building correction models that correct natural language snippets. Correction models can include rules comprising pairs of word sequences identified from viable correction snippet pairs, where a first sequence of words in the pair should be replaced with a second sequence of words in the pair. Viable correction snippet pairs can be identified from among pairs of language snippets, such as a post to a social media website and a subsequent update to that post. Viable corrections can be the snippet pairs that both have no more unaligned words than a word alignment threshold and have no aligned word pair with a character edit difference above an edit distance threshold. In some implementations, word alignments can be found by aligning all the characters between a pair of language snippets, and identifying aligned words as those that have at least one aligned letter in common.

Abstract: This disclosure generally covers systems and methods that schedule posts of one or more images, videos, or other digital content items through an account of a social networking system in a way that optimizes user engagement. In particular, certain embodiments of the disclosed systems and methods determine when to post digital content items by calculating data-driven user engagement scores and updating those scores with data tracked by the social networking system.

Abstract: Technology is disclosed for building correction models that correct natural language snippets. Correction models can include rules comprising pairs of word sequences identified from viable correction snippet pairs, where a first sequence of words in the pair should be replaced with a second sequence of words in the pair. Viable correction snippet pairs can be identified from among pairs of language snippets, such as a post to a social media website and a subsequent update to that post. Viable corrections can be the snippet pairs that both have no more unaligned words than a word alignment threshold and have no aligned word pair with a character edit difference above an edit distance threshold. In some implementations, word alignments can be found by aligning all the characters between a pair of language snippets, and identifying aligned words as those that have at least one aligned letter in common.

Abstract: Technology is disclosed for building correction models that correct natural language snippets. Correction models can include rules comprising pairs of word sequences identified from viable correction snippet pairs, where a first sequence of words in the pair should be replaced with a second sequence of words in the pair. Viable correction snippet pairs can be identified from among pairs of language snippets, such as a post to a social media website and a subsequent update to that post. Viable corrections can be the snippet pairs that both have no more unaligned words than a word alignment threshold and have no aligned word pair with a character edit difference above an edit distance threshold. In some implementations, word alignments can be found by aligning all the characters between a pair of language snippets, and identifying aligned words as those that have at least one aligned letter in common.

Abstract: Systems and methods for clarifying a computer user's intent with regard to one or more query terms of a search query are presented. Often, one or more query terms of a search query are ambiguous and/or unclear. As presented herein, upon identifying a query term in the search query that is unclear, clarification data is obtained and presented to the computer user by way of a clarification user interface. The computer user is able to identify one or more aspects of the computer user's intent, intent clarifications, with regard to the query term through the clarification user interface. Upon instruction by the computer user, the search query, along with the intent clarifications, is submitted to a search engine and the search results obtained from the search engine are presented to the computer user.