We propose a novel interpretable deep neural network for text classification, called ProtoryNet, based on a new concept of prototype trajectories. Motivated by the prototype theory in modern linguistics, ProtoryNet makes a prediction by finding the most similar prototype for each sentence in a text sequence and feeding an RNN backbone with the proximity of each sentence to the corresponding active prototype. The RNN backbone then captures the temporal pattern of the prototypes, which we refer to as prototype trajectories. Prototype trajectories enable intuitive and fine-grained interpretation of the reasoning process of the RNN model, in resemblance to how humans analyze texts. We also design a prototype pruning procedure to reduce the total number of prototypes used by the model for better interpretability. Experiments on multiple public datasets demonstrate that ProtoryNet achieves higher accuracy than the baseline prototype-based deep neural net and narrows the performance gap when compared to state-of-the-art black-box models. In addition, after prototype pruning, the resulting ProtoryNet models only need less than or around 20 prototypes for all datasets, which significantly benefits interpretability. Furthermore, we report survey results indicating that human users find ProtoryNet more intuitive and easier to understand compared to other prototype-based methods.

Recurrent neural networks (RNN) are widely used for handling sequence data. However, their black-box nature makes it difficult for users to interpret the decision-making process. We propose a new method to construct deterministic finite automata to explain RNN. In an automaton, states are abstracted from hidden states produced by the RNN, and the transitions represent input symbols. Thus, users can follow the paths of transitions, called patterns, to understand how a prediction is produced. Existing methods for extracting automata partition the hidden state space at the beginning of the extraction, which often leads to solutions that are either inaccurate or too large in size to comprehend. Unlike previous methods, our approach allows the automata states to be formed adaptively during the extraction. Instead of defining patterns on pre-determined clusters, our proposed model, AdaAX, identifies small sets of hidden states determined by patterns with finer granularity in data. Then these small sets are gradually merged to form states, allowing users to trade fidelity for lower complexity. Experiments show that our automata can achieve higher fidelity while being significantly smaller in size than baseline methods on synthetic and complex real datasets.

Abstract There is a natural tension between the desire to share information and keep sensitive information private on online social media. Privacy seeking social media users may seek to keep their location private by avoiding the mentions of location revealing words such as points of interest (POIs), believing this to be enough. In this paper, we show that it is possible to uncover the location of a social media user’s post even when it is not geotagged and does not contain any POI information. Our proposed approach Jasoosachieves this by exploiting the shared vocabulary between users who reveal their location and those who do not. To this end, Jasoosuses a variant of the Naive Bayes algorithm to identify location revealing words or hashtags based on both temporal and atemporal perspectives. Our evaluation using tweets collected from four different states in the United States shows that Jasooscan accurately infer the locations of close to half a million tweets corresponding to more than 20,000 distinct users (i.e., more than 50% of the test users) from the four states. Our work demonstrates that location privacy leaks do occur despite due precautions by a privacy conscious user. We design and evaluate countermeasures based Jasoosto mitigate location privacy leaks.