most citedInformation Extraction in Illicit Domains

30 citations · 59 across the 6 of their papers we have counts for

collaborators

6 papers

cs.IR20181 cited

Unsupervised Hashtag Retrieval and Visualization for Crisis Informatics

Yao Gu, Mayank Kejriwal

In social media like Twitter, hashtags carry a lot of semantic information and can be easily distinguished from the main text. Exploring and visualizing the space of hashtags in a…

cs.IR20182 cited

A Pipeline for Post-Crisis Twitter Data Acquisition

Mayank Kejriwal, Yao Gu

Due to instant availability of data on social media platforms like Twitter, and advances in machine learning and data management technology, real-time crisis informatics has emerge…

cs.CY201710 cited

FlagIt: A System for Minimally Supervised Human Trafficking Indicator Mining

Mayank Kejriwal, Jiayuan Ding, Runqi Shao +2

In this paper, we describe and study the indicator mining problem in the online sex advertising domain. We present an in-development system, FlagIt (Flexible and adaptive generatio…

cs.AI20175 cited

Always Lurking: Understanding and Mitigating Bias in Online Human Trafficking Detection

Kyle Hundman, Thamme Gowda, Mayank Kejriwal +1

Web-based human trafficking activity has increased in recent years but it remains sparsely dispersed among escort advertisements and difficult to identify due to its often-latent n…

cs.CL201711 cited

Supervised Typing of Big Graphs using Semantic Embeddings

Mayank Kejriwal, Pedro Szekely

We propose a supervised algorithm for generating type embeddings in the same semantic vector space as a given set of entity embeddings. The algorithm is agnostic to the derivation…

cs.CL201730 cited

Information Extraction in Illicit Domains

Mayank Kejriwal, Pedro Szekely

Extracting useful entities and attribute values from illicit domains such as human trafficking is a challenging problem with the potential for widespread social impact. Such domain…