@conference {333, title = {A Socio-linguistic Model for Cyberbullying Detection}, booktitle = {International Conference on Advances in Social Networks Analysis and Mining (ASONAM)}, year = {2018}, abstract = {

Cyberbullying is a serious threat to both the short and long-term well-being of social media users. Addressing this problem in online environments demands the ability to automatically detect cyberbullying and to identify the roles that participants assume in social interactions. As cyberbullying occurs within online communities, it is also vital to understand the group dynamics that support bullying behavior. To this end, we propose a socio-linguistic model which jointly detects cyberbullying content in messages, discovers latent text categories, identifies participant roles and exploits social interactions. While our method makes use of content that is labeled as bullying, it does not require category, role or relationship labels. Furthermore, as bullying labels are often subjective, noisy and inconsistent, an important contribution of our paper is effective methods for leveraging inconsistent labels. Rather than discard inconsistent labels, we evaluate different methods for learning from them, demonstrating that incorporating uncertainty allows for better generalization. Our proposed socio-linguistic model achieves an 18\% improvement over state-of-the-art methods.

}, author = {Tomkins, Sabina and Lise Getoor and Chen, Yunfei and Zhang, Yi} } @conference {335, title = {Sustainability at Scale: Bridging the Intention-Behavior Gap with Sustainable Recommendations}, booktitle = {Recommender Systems (RecSys)}, year = {2018}, abstract = {

Finding sustainable products and evaluating their claims is a significant barrier facing sustainability-minded customers. Tools that reduce both these burdens are likely to boost the sale of sustainable products. However, it is difficult to determine the sustainability characteristics of these products {\textemdash} there are a variety of certifications and definitions of sustainability, and quality labeling requires input from domain experts. In this paper, we propose a flexible probabilistic framework that uses domain knowledge to identify sustainable products and customers, and uses these labels to predict customer purchases. We evaluate our approach on grocery items from the Amazon catalog. Our proposed approach outperforms established recommender system models in predicting future purchases while jointly inferring sustainability scores for customers and products.

}, author = {Tomkins, Sabina and Isley, Steve and London, Ben and Lise Getoor} } @conference {334, title = {The Impact of Environmental Stressors on Human Trafficking}, booktitle = {ICWSM Workshop on Beyond Online Data (BOD)}, year = {2018}, abstract = {

Severe environmental events have extreme effects on all segments of society, including criminal activity. Extreme weather events, such as tropical storms, fires, and floods create instability in communities, and can be exploited by criminal organizations. Here we investigate the potential impact of catastrophic storms on the criminal activity of human trafficking. We propose three theories of how these catastrophic storms might impact trafficking and provide evidence for each. Researching human trafficking is made difficult by its illicit nature and the obscurity of high-quality data. Here, we analyze online advertisements for services which can be collected at scale and provide insights into traffickers{\textquoteright} behavior. To successfully combine relevant heterogenous sources of information, as well as spatial and temporal structure, we propose a collective, probabilistic approach. We implement this approach with Probabilistic Soft Logic, a probabilistic programming framework which can flexibly model relational structure and for which inference of future locations is highly efficient. Furthermore, this framework can be used to model hidden structure, such as latent links between locations. Our proposed approach can model and predict how traffickers move. In addition, we propose a model which learns connections between locations. This model is then adapted to have knowledge of environmental events, and we demonstrate that incorporating knowledge of environmental events can improve prediction of future locations. While we have validated our models on the impact of severe weather on human trafficking, we believe our models can be generalized to a variety of other settings in which environmental events impact human behavior.

}, author = {Tomkins, Sabina and Golnoosh Farnadi and Brian Amantullah and Lise Getoor and Steven Minton} } @conference {338, title = {The Impact of Environmental Stressors on Human Trafficking}, booktitle = {International Conference on Data Mining (ICDM)}, year = {2018}, abstract = {

{\textemdash}Severe environmental events have extreme effects on all segments of society, including criminal activity. Extreme weather events, such as tropical storms, fires, and floods create instability in communities, and can be exploited by criminal organizations. Here we investigate the potential impact of catastrophic storms on the criminal activity of human trafficking. We propose three theories of how these catastrophic storms might impact trafficking and provide evidence for each. Researching human trafficking is made difficult by its illicit nature and the obscurity of high-quality data. Here, we analyze online advertisements for services which can be collected at scale and provide insights into traffickers{\textquoteright} behavior. To successfully combine relevant heterogenous sources of information, as well as spatial and temporal structure, we propose a collective, probabilistic approach. We implement this approach with Probabilistic Soft Logic, a probabilistic programming framework which can flexibly model relational structure and for which inference of future locations is highly efficient. Furthermore, this framework can be used to model hidden structure, such as latent links between locations. Our proposed approach can model and predict how traffickers move. In addition, we propose a model which learns connections between locations. This model is then adapted to have knowledge of environmental events, and we demonstrate that incorporating knowledge of environmental events can improve prediction of future locations. While we have validated our models on the impact of severe weather on human trafficking, we believe our models can be generalized to a variety of other settings in which environmental events impact human behavior

}, author = {Tomkins, Sabina and Golnoosh Farnadi and Brian Amantullah and Lise Getoor and Steven Minton} } @conference {tomkins:lld2017, title = {Detecting Cyber-bullying from Sparse Data and Inconsistent Labels}, booktitle = {Learning with Limited Labeled Data (LLD) NIPS Workshop}, year = {2017}, author = {Tomkins, Sabina and Lise Getoor and Chen, Yunfei and Zhang, Yi} } @conference {tomkins:ijcai17, title = {Disambiguating Energy Disaggregation: A Collective Probabilistic Approach}, booktitle = {International Joint Conference on Artifi cial Intelligence}, year = {2017}, author = {Tomkins, Sabina and Pujara, Jay and Lise Getoor} }