Adobe, Inc.

Vineet Gupta

Sayaji Hande

Balaji Vasan Srinivasan

Ritwik Sinha

Anandhavelu Natarajan

Shriram Revankar

Sandeep Zechariah George Kollannur

Harsh Jhamtani

Anuj Jain

Ramesh Srinivasaraghavan

Balaraman Ravindran

Deepam Kedia

Shweta M Karwa

Anand Ganesan

Japnik Singh

Bharat Polineni

Sandeep Zechariah George

Devesh Varshney

This document describes techniques for predicting spread of content across social networks. In various embodiments, user interactions with content posted to a social network are accessed during a first stage. The accessed user interactions are applied to a prediction model to predict future user interactions with the content during one or more subsequent stages.

Predicting spread of content across social networks

This document describes techniques for normalizing user responses by removing behavioral bias. In one or more implementations, a response normalization model is built from user responses to one or more events. The response normalization model clusters users into behavioral classification groups based on the user responses. The response normalization model can then be used to normalize user responses by removing behavioral bias from user responses.

Normalizing user responses to an event by removing behavioral bias

A method and apparatus for prediction of community reaction to a post for an online social community is disclosed. The method comprises receiving a proposed post as input to a generated prediction model prior to the proposed post being posted to an online social community; predicting a community reaction to the proposed post using the prediction model; and displaying the predication, wherein the prediction comprises a sentiment score and at least one of a number of responses, a number of responders to the post, a longevity of the post, or a half-life of the post.

Method and apparatus for prediction of community reaction to a post

Determined seed groups herein improve content dissemination across a communication network connecting a plurality of users. Probabilities of each user in the plurality influencing remaining users in the plurality to observe the content are identified to select a first influential user from the plurality. The seed group size is established and a user of the plurality with a probability proximate to the probability of the first influential user is identified. Based on the seed group size, the probabilities of the remaining users in the plurality are unified with the probability of the first influential user to determine new probabilities of the remaining users, and another user of the plurality with a probability proximate to the probability of the first influential user is identified. The method then provides for selecting the users identified as having probabilities proximate to the probability of the first influential user to establish the seed group.

Seed group selection in a probabilistic network to increase content dissemination

This document describes techniques for identifying suggestive intent in social posts. In one or more implementations, a topic is received and social posts to one or more social networks that are related to the topic are collected. Then, one or more suggestive intent posts expressing suggestions towards the topic are identified from the collected social posts. In one or more implementations, a set of related topics are received and social posts to one or more social networks that correspond to the related topics are collected. Then, aspects corresponding to the related topics are identified, and a sentiment score is generated for each aspect of each related topic that can be used to compare aspects of the related topics. In one or more implementations a suggestive intent model, usable to identify social posts expressing suggestive intent, is built from a training corpus of annotated social posts.

Identifying suggestive intent in social posts

This document describes techniques for predicting reactions to short-text posts. In one or more implementations, a prediction model for short-text posts is generated from previous posts to a social network and responses to the posts by the social network community. Subsequently, the prediction model can be used to predict the social network community's reaction to a proposed post prior to the proposed post being posted to the social network.

Predicting reactions to short-text posts

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Kanpur, India

Vineet Gupta