Interpreting T witter User Geolocation (2020)

Abstract: Identifying user geolocation in online social networks is an essential task in many location-based applications. Existing methods rely on the similarity of text and network structure, however, they suffer from a lack of interpretability on the corresponding results, which is crucial for understanding model behavior. In this work, we adopt influence functions to interpret the behavior of GNN-based models by identifying the importance of training users when predicting the locations of the testing users. This methodology helps with providing meaningful explanations on prediction results. Furthermore, it also initiates an attempt to uncover the so-called “black-box” GNN-based models by investigating the effect of individual nodes.

## introduction identifying geographic locations of users in online social networks (osn) has become a key internet service for many downstream applications, including location-based targeted advertising, emergency location identification, political election campaign, local event/place recommendation, natural disaster response, and remediation, etc. @xcite . as such, the problem of user geolocation (ug) has received a great deal of research attention in the past decade @xcite @xcite @xcite . earlier efforts @xcite @xcite @xcite @xcite mainly focused on extracting indicative information from user-posted contents. these approaches rely on informative words that can link users to their specific locations via various natural language processing techniques such as topic modeling and other statistical models. more recently, researchers aimed at incorporating multi-aspect information, especially the user mention/interaction network to boost the performance of geolocation identification @xcite @xcite @xcite . for example, @xcite employ graph convolutional networks (gcns) (kipf and welling, 2017) or simplified gcn @xcite to learn network structures for user geolocation. in addition, graph representation-based methods @xcite @xcite @xcite have also been widely used for user geolocation @xcite @xcite @xcite . however, the existing methods lack model transparency and fail to provide meaningful explanations regarding the model behavior and prediction results, which prevents them from safety-critical applications. for example, when locating an emergency for a specific region, it would be more meaningful to explain why such prediction is made, rather than simply presenting numerical ranking values. to address such limitations, we propose a general framework to explain the behavior of user geolocation models and the prediction results, by utilizing the influence function @xcite to quantify the impact of in-network users on the predicted outcome. the main assumption is that the prediction results from a trained geolocation model are typically affected by the knowledge learned from training data (i.e., all in-network users and their associated attributes). we demonstrate that the user geolocation (especially) for the network-based methods, is largely dominated by the geographical locations of the 1hop neighboring nodes. this finding, on the one hand, enables demystification of the model behavior and quantitative measuring of the influence of individual users (both 1-hop and high-order prox-imity nodes); and on the other hand, provides interpretation on the predicted locations for downstream applications/decision makers -which, in turn, points out a direction to further improve the model. ## preliminaries the user geolocation problem is defined as predicting the user's "home" location @xcite . since each location is described by a (longitude, latitude) pair, the task can be converted into a typical classification problem where the labels/categories are region ids obtained through some space-partitioning methods, such as k-d trees, that divides the surface of the earth into closed and non-overlapping regions. each user is associated with one (and only one) region-label that she/he belongs to. we use one-hot encoding to denote each label @xmath0, where c is the number of regions. the user geolocation task thus becomes one of identifying the geographical locations, given the user tweet contents x and the mention network g. graph neural networks (gnns) @xcite @xcite @xcite @xcite are powerful tools of representation learning for graph data, which has received increasing attention over the past several years @xcite . the main idea is that, given a network @xmath1, e) with attributes x @xmath2, a general gnn is to learn a vector representation of node v by: ## interpreting geolocation results previous works @xcite @xcite typically leverage graph embedding methods @xcite @xcite for user representation learning. in spite of the superior performance, gnns (including those used for user geolocation) @xcite model the process of learning and prediction in a "black-box" manner and, consequently, are limited in terms of interpretability. therefore, it is important to understand how the model learns the data and why a particular prediction is made. to demystify the geolocation models, we intend to uncover and explain the training process and prediction outcomes. there has been a growing interest to explain the model behavior and the predicted outcomes in the area of neural networks. in particular, an influence function @xcite is used to estimate the effect of individual training sample and interpret the results made by a specific model. while providing an elegant post-training interpretation, computationallyprohibitive cost of repeatedly retraining the model is avoided, spurring wide uses in a range of applications, including image classification (koh and liang, 2017), group effect (koh et al., 2019), recommendation @xcite , etc. however, whether influence functions can be applied to gnnbased models remained unclear. we take a step towards bridging this gap by tracing the geolocation results from the gnn-based models back to the important nodes in the mention network. specifically, let z 1 , . . . , z m denote the m training samples (nodes in the mention network g), where each z @xmath3, y i ) is an attribute vector of i-th node and its label. removing one sample z from the training set can change the model optimal parameters from θ * to θ * -z , where θ * and θ * -z is the set of optimal parameters with and without the sample z, respectively. and θ * -z can be obtained by: θ * -z @xmath4, θ), where ℓ(z i , θ) is the loss of sample z i . to estimate the influence of every removed training sample z and avoid retraining the model, @xcite use an influence function strategy to efficiently approximate this behavior. the basic idea is to compute the change of optimal parameters if z was upweighted by some small , which gives the new parameters: to speed up the computation, we use implicit hessian-vector products (hvps) to approximate w test @xmath5, θ * ), and eq. ( 3 ) can be rewritten as i up,loss (z, z test ) = -w test θ ℓ(z, θ * ). since the hessian h θ * is positive semi-definite by assumption, we have: ## experiments we now discuss in detail our experimental findings. ## conclusion in this work, we presented a framework for explaining the gnn-based models by extending the influence function to estimate the effect of samples in graph data. the experiments conducted on a specific task -user geolocation -provided intuitive explanations and enabled quantification of the influence of individual training samples. some interesting observations include the effects of regions and the sensitivity of gnn-based models, which open potentials for further improvements that we plan to address in our future work.