Understanding Social Spammers: A Data Mining Perspective
Xia “Ben” Hu

Computer Science and Engineering
Arizona State University

12:00-1:00 Tuesday, 24 February 2015

With the growing popularity of social media, social spamming has become rampant on all platforms. Many (fake) accounts, known as social spammers, are employed to overwhelm legitimate users with unwanted information. Social spammers are unique due to their coordinated efforts to launch attacks such as distributing ads to generate sales, disseminating pornography and viruses, executing phishing attacks, or simply sabotaging a system’s reputation. In this talk, I will introduce a novel and systematic analysis of social spammers from a data mining perspective to tackle the challenges raised by social media data for spammer detection. Specifically, I will formally define the problem of social spammer detection and discuss the unique properties of social media data that make this problem challenging. By analyzing the two most important types of information, network and content information, I will introduce a unified framework by collectively using heterogeneous information in social media. To tackle the labeling bottleneck in social media, I will show how we can take advantage of the existing information about spam in email, SMS, and on the web for spammer detection in microblogging. I will also present a solution for efficient online processing to handle fast-evolving social spammers.

Xia Hu is a Ph.D. candidate in Computer Science and Engineering at Arizona State University, supervised by Professor Huan Liu. His research interests include data mining, machine learning, social network analysis, etc. As a result of his research work, he has published nearly 40 papers in several major academic venues, including WWW, SIGIR, KDD, WSDM, IJCAI, AAAI, CIKM, SDM, etc. One of his papers was selected for the Best Paper Shortlist in WSDM’13. He is the recipient of IEEE “Atluri Award” Scholarship, 2014 ASU’s President’s Award for Innovation, and Faculty Emeriti Fellowship. He has served on program committees for several major conferences such as WWW, IJCAI, SDM and ICWSM, and reviewed for multiple journals, including IEEE TKDE, ACM TOIS and Neurocomputing. His research attracts wide range of external government and industry sponsors, including NSF, ONR, AFOSR, Yahoo!, and Microsoft.

– more information and directions: http://bit.ly/UMBCtalks –

UMBC’s Eco-Ambassadors welcome you back to campus and the spring semester!  We will continue our "Turn Down Tuesdays" series this semester with energy saving fun facts and tips!

As the semester’s workload begins to take off, we probably find ourselves using our computers more often.  Set your computer to automatically go to sleep after 10-15 minutes to save energy.  

According to energy expert Michael Bluejay, an average laptop uses 15-60 watts during normal use, but uses only 0-6 watts in sleep mode and 0 watts in hibernate.  That may not seem like a lot of savings, but imagine if everyone did this…it would add up!

Also, skip the screen saver – instead of saving energy, it consumes energy.  Setting your computer to sleep will still allow you to start up quickly where you left off.

Topic Modeling with Structured Priors for Text-Driven Science

Michael Paul, JHU

12:00pm – 1:00pm, Monday, 2 March 2015, ITE 325

Many scientific disciplines are being revolutionized by the explosion of public data on the web and social media, particularly in health and social sciences. For instance, by analyzing social media messages, we can instantly measure public opinion, understand population behaviors, and monitor events such as disease outbreaks and natural disasters. Taking advantage of these data sources requires tools that can make sense of massive amounts of unstructured and unlabeled text. Topic models, statistical models that describe low-dimensional representations of data, can uncover interesting latent structure in large text datasets and are popular tools for automatically identifying prominent themes in text. However, to be useful in scientific analyses, topic models must learn interpretable patterns that accurately correspond to real-world concepts of interest.

In this talk, I will introduce Sprite, a family of topic models that can encode additional structures such as hierarchies, factorizations, and correlations, and can incorporate supervision and domain knowledge. Sprite extends standard topic models by formulating the Bayesian priors over parameters as functions of underlying components, which can be constrained in various ways to induce different structures. This creates a unifying representation that generalizes several existing topic models, while creating a powerful framework for building new models. I will describe a few specific instantiations of Sprite and show how these models can be used in various scientific applications, including extracting self-reported information about drugs from web forums, analyzing healthcare quality in online reviews, and summarizing public opinion in social media on issues such as gun control.

Michael Paul is a PhD candidate in Computer Science at Johns Hopkins University. He earned an M.S.E. in CS from Johns Hopkins University in 2012 and a B.S. in CS from the University of Illinois at Urbana-Champaign in 2009. He has received PhD fellowships from Microsoft Research, the National Science Foundation, and the Johns Hopkins University Whiting School of Engineering. His research focuses on exploratory machine learning and natural language processing for the web and social media, with applications to computational epidemiology and public health informatics.

– more information and directions: http://bit.ly/UMBCtalks –

Richard Warren “Jake” Jaquish (1933–1999) was a passionate landscape photographer for whom making photographs was a spiritual quest. Being out in the middle of a wilderness area gave him great satisfaction especially when he made images that touched upon something elemental in the human spirit. The primordial landscape produced in him a heightened awareness of matters only explainable in terms of images.

Trained in photography at Rochester Institute of Technology (RIT) during the late 1950s, Jaquish studied with notables such as Minor White and Beaumont Newhall. He graduated in 1960 taught elsewhere, and soon came to Baltimore to teach at the Maryland Institute College of Art. After eighteen years, he left teaching to work as a professional photographer for the Maryland State Department of Transportation where he was able to earn a living making his beloved landscape photographs.

The Richard Jaquish Archive was given to UMBC by Alwilda Scholler Jaquish, and supported by the Richard and Alwilda Scholler Jaquish Endowment. The show was produced by Tom Beck, Chief Curator, and Jazmin Smith, UMBC ’14.

Eric Dyer, Visual Arts, is featured in Wave & Particle, a group exhibition that celebrates Creative Capital’s fifteenth anniversary, at Ronald Feldman Fine Arts in New York. The exhibition opened on Saturday, February 14 and will continue on display through March 21. More information is available by clicking here.

His work will also be featured in Moving Image New York, a group exhibition on display at the Waterfront Tunnel in Chelsea in New York, from March 5 through 8. Additional information about Moving Image is available by clicking here.