Abstract: A noise reduction and smart ticketing application for social media-based communication systems may identify social media-based communications from users who are attempting to engage with a brand or entity on a social media platform as actionable, and distinguish other communications as noise. The noise reduction and smart ticketing system may use machine learning to determine which social media communications are actionable for a given company or other organization, and generates tickets for actionable communications. Actionable communications may include, but are not limited to, technical support issues, inquiries about a product release date, grievances, incidents, suggestions to improve service, critiques of company policies, etc. Non-actionable communications (i.e., “noise”) may include, but are not limited to, suggestions to other users, promotions, coupons, offers, marketing campaigns, affiliate marketing, statements that a user is attending an event, etc.

Abstract: A process for extracting and recognizing named entities from a short unstructured chat-style text input. The process may tokenize an inbound electronic message, and use a combination of entity specific classifiers and databases comprising known named entities such as gazetteer(s) to identify one or more named entities within the inbound electronic message. The identified named entities are then compiled as response message and transmitted to the user.

Abstract: A noise reduction and smart ticketing application for social media-based communication systems may identify social media-based communications from users who are attempting to engage with a brand or entity on a social media platform as actionable, and distinguish other communications as noise. The noise reduction and smart ticketing system may use machine learning to determine which social media communications are actionable for a given company or other organization, and generates tickets for actionable communications. Actionable communications may include, but are not limited to, technical support issues, inquiries about a product release date, grievances, incidents, suggestions to improve service, critiques of company policies, etc. Non-actionable communications (i.e., “noise”) may include, but are not limited to, suggestions to other users, promotions, coupons, offers, marketing campaigns, affiliate marketing, statements that a user is attending an event, etc.

Abstract: Large batches of social media communications may be automatically annotated. This provides techniques to create large labeled datasets without the assistance of human labelers. For instance, social media communications may be fetched and annotated as actionable or noise for a given account (e.g., a brand handle on Twitter®) without human review. Social media communications from users who are attempting to engage with a brand or entity on a social media platform may be annotated as actionable, whereas other communications may be labeled as noise.

Abstract: Large batches of social media communications may be automatically annotated. This provides techniques to create large labeled datasets without the assistance of human labelers. For instance, social media communications may be fetched and annotated as actionable or noise for a given account (e.g., a brand handle on Twitter®) without human review. Social media communications from users who are attempting to engage with a brand or entity on a social media platform may be annotated as actionable, whereas other communications may be labeled as noise.

Abstract: A process for extracting and recognizing named entities from a short unstructured chat-style text input. The process may tokenize an inbound electronic message, and use a combination of entity specific classifiers and databases comprising known named entities such as gazetteer(s) to identify one or more named entities within the inbound electronic message. The identified named entities are then compiled as response message and transmitted to the user.

Abstract: A noise reduction and smart ticketing application for social media-based communication systems may identify social media-based communications from users who are attempting to engage with a brand or entity on a social media platform as actionable, and distinguish other communications as noise. The noise reduction and smart ticketing system may use machine learning to determine which social media communications are actionable for a given company or other organization, and generates tickets for actionable communications. Actionable communications may include, but are not limited to, technical support issues, inquiries about a product release date, grievances, incidents, suggestions to improve service, critiques of company policies, etc. Non-actionable communications (i.e., “noise”) may include, but are not limited to, suggestions to other users, promotions, coupons, offers, marketing campaigns, affiliate marketing, statements that a user is attending an event, etc.

Abstract: A noise reduction and smart ticketing application for social media-based communication systems may identify social media-based communications from users who are attempting to engage with a brand or entity on a social media platform as actionable, and distinguish other communications as noise. The noise reduction and smart ticketing system may use machine learning to determine which social media communications are actionable for a given company or other organization, and generates tickets for actionable communications. Actionable communications may include, but are not limited to, technical support issues, inquiries about a product release date, grievances, incidents, suggestions to improve service, critiques of company policies, etc. Non-actionable communications (i.e., “noise”) may include, but are not limited to, suggestions to other users, promotions, coupons, offers, marketing campaigns, affiliate marketing, statements that a user is attending an event, etc.

Abstract: A machine-implemented method for detecting scareware includes the steps of accessing one or more landing pages to be evaluated, extracting one or more features from the landing pages, and providing a classifier to compare the features extracted from the landing pages with features of known scareware and non-scareware pages. The classifier determines a likelihood that the landing page is scareware. If determined to be scareware, the landing page is removed from search results generated by a search engine. The features can be URLs, text, image interest points, image descriptors, a number of pop-ups generated, IP addresses, hostnames, domain names, text derived from images, images, metadata, identifiers of executables, and combinations thereof.

Abstract: A machine-implemented method for detecting scareware includes the steps of accessing one or more landing pages to be evaluated, extracting one or more features from the landing pages, and providing a classifier to compare the features extracted from the landing pages with features of known scareware and non-scareware pages. The classifier determines a likelihood that the landing page is scareware. If determined to be scareware, the landing page is removed from search results generated by a search engine. The features can be URLs, text, image interest points, image descriptors, a number of pop-ups generated, IP addresses, hostnames, domain names, text derived from images, images, metadata, identifiers of executables, and combinations thereof.