Hemank Lamba

A Tempest in a Teacup? Analyzing Firestorms on Twitter

`Firestorms, sudden bursts of negative attention in cases of controversy and outrage, are seemingly widespread on Twitter and are an increasing source of fascination and anxiety in the corporate, governmental, and public spheres. Using media mentions, we collect 80 candidate events from January 2011 to September 2014 that we would term `firestorms. Using data from the Twitter decahose (or gardenhose), a 10% random sample of all tweets, we describe the size and longevity of these firestorms. We take two firestorm exemplars, #myNYPD and #CancelColbert, as case studies to describe more fully. Then, taking the 20 firestorms with the most tweets, we look at the change in mention networks of participants over the course of the firestorm as one method of testing for possible impacts of firestorms. We find that the mention networks before and after the firestorms are more similar to each other than to those of the firestorms, suggesting that firestorms neither emerge from existing networks, nor do they result in lasting changes to social structure. To verify this, we randomly sample users and generate mention networks for baseline comparison, and find that the firestorms are not associated with a greater than random amount of change in mention networks.

Incorporating Side Information in Tensor Completion

Matrix and tensor completion techniques have proven useful in many applications such as recommender systems, image/video restoration, and web search. We explore the idea of using external information in completing missing values in tensors. In this work, we present a framework that employs side information as kernel matrices for tensor factorization. We apply our framework to problems of recommender systems and video restoration and show that our framework effectively deals with the cold-start problem.

Experience-Aware Item Recommendation in Evolving Review Communities

Current recommender systems exploit user and item similarities by collaborative filtering. Some advanced methods also consider the temporal evolution of item ratings as a global background process. However, all prior methods disregard the individual evolution of a users experience level and how this is expressed in the users writing in a review community. In this paper, we model the joint evolution of user experience, interest in specific item facets, writing style, and rating behavior. This way we can generate individual recommendations that take into account the users maturity level (e.g., recommending art movies rather than blockbusters for a cinematography expert). As only item ratings and review texts are observables, we capture the users experience and interests in a latent model learned from her reviews, vocabulary and writing style. We develop a generative HMM-LDA model to trace user evolution, where the Hidden Markov Model (HMM) traces her latent experience progressing over time -- with solely user reviews and ratings as observables over time. The facets of a users interest are drawn from a Latent Dirichlet Allocation (LDA) model derived from her reviews, as a function of her (again latent) experience level. In experiments with four realworld datasets, we show that our model improves the rating prediction over state-of-the-art baselines, by a substantial margin. In addition, our model can also give some interpretations for the user experience level.Current recommender systems exploit user and item similarities by collaborative filtering. Some advanced methods also consider the temporal evolution of item ratings as a global background process. However, all prior methods disregard the individual evolution of a users experience level and how this is expressed in the users writing in a review community. In this paper, we model the joint evolution of user experience, interest in specific item facets, writing style, and rating behavior. This way we can generate individual recommendations that take into account the users maturity level (e.g., recommending art movies rather than blockbusters for a cinematography expert). As only item ratings and review texts are observables, we capture the users experience and interests in a latent model learned from her reviews, vocabulary and writing style. We develop a generative HMM-LDA model to trace user evolution, where the Hidden Markov Model (HMM) traces her latent experience progressing over time -- with solely user reviews and ratings as observables over time. The facets of a users interest are drawn from a Latent Dirichlet Allocation (LDA) model derived from her reviews, as a function of her (again latent) experience level. In experiments with five real-world datasets, we show that our model improves the rating prediction over state-of-the-art baselines, by a substantial margin. We also show, in a use-case study, that our model performs well in the assessment of user experience levels.

Maximizing the Spread of Positive Influence by Deadline

Influence maximization has found applications in various fields such as sensor placement, viral marketing, controlling rumor outbreak, etc. In this paper, we propose a targeted approach to influence maximization in polarized networks i.e. networks where we already know or can predict nodes opinion about a product or topic. The goal is to find a set of individuals to target, such that positive opinion about a specific topic or the product to be launched is maximized. Another key aspect that is present in most of the existing viral marketing algorithms is that they do not take into account the timeliness of the product adoption. In this paper, we present a framework where we infer the polarity, activity levels of the users, and then select seeds to launch viral marketing campaigns such that positive influence about the product is maximized by the given deadline.

Model-based Cluster Analysis for Identifying Suspicious Activity Sequences in Software

Large software systems have to contend with a significant number of users who interact with different components of the system in various ways. The sequences of components that are used as part of an interaction define sets of behaviors that users have with the system. These can be large in number. Among these users, it is possible that there are some who exhibit anomalous behaviors -- for example, they may have found back doors into the system and are doing something malicious. These anomalous behaviors can be hard to distinguish from normal behavior because of the number of interactions a system may have, or because traces may deviate only slightly from normal behavior. In this paper we describe a model-based approach to cluster sequences of user behaviors within a system and to find suspicious, or anomalous, sequences. We exploit the underlying software architecture of a system to define these sequences. We further show that our approach is better at detecting suspicious activities than other approaches, specifically those that use unigrams and bigrams for anomaly detection. We show this on a simulation of a large scale system based on Amazon Web application style architecture.

xStream: Outlier Detection in Feature-Evolving Data Streams

This work addresses the outlier detection problem for feature-evolving streams, which has not been studied before. In this setting both (1) data points may evolve, with feature values changing, as well as (2) feature space may evolve, with newly-emerging features over time. This is notably different from row-streams, where points with fixed features arrive one at a time. We propose a density-based ensemble outlier detector, called xStream, for this more extreme streaming setting which has the following key properties: (1) it is a constant-space and constant-time (per incoming update) algorithm, (2) it measures outlierness at multiple scales or granularities, it can handle (3 i ) high-dimensionality through distance-preserving projections, and (3

Stop the KillFies! Using Deep Learning Models to Identify Dangerous Selfies

ii

zooRank: Ranking Suspicious Entities in Time-Evolving Tensors

) non-stationarity via

A model-based approach to anomaly detection in software architectures

O(1)

Man-O-Meter: Modeling and Assessing the Evolution of Language Usage of Individuals on Microblogs

-time model updates as the stream progresses. In addition, xStream can address the outlier detection problem for the (less general) disk-resident static as well as row-streaming settings. We evaluate xStream rigorously on numerous real-life datasets in all three settings: static, row-stream, and feature-evolving stream. Experiments under static and row-streaming scenarios show that xStream is as competitive as state-of-the-art detectors and particularly effective in high-dimensions with noise. We also demonstrate that our solution is fast and accurate with modest space overhead for evolving streams, on which there exists no competition.