OpenRaiser/Intern-Atlas huggingface dataset:
Towards Robust False Information Detection on Social Networks with Contrastive Learning
Authors: Guanghui Ma, Chunming Hu, Ling Ge, Junfan Chen, Hong Zhang, Richong Zhang
Venue: CIKM
Year: 2022

Constructing a robust conversation graph based false information detection model is crucial for real social platforms. Recently, graph neural network (GNN) methods for false information detection have achieved significant advances. However, we empirically find that slight perturbations in the conversation graph can cause the predictions of existing models to collapse. To address this problem, we present RDCL, a contrastive learning framework for false information detection on social networks, to obtain robust detection results. RDCL leverages contrastive learning to maximize the consistency between perturbed graphs from the same original graph and minimize the distance between perturbed and original graphs from the same class, forcing the model to improve resistance to data perturbations. Moreover, we prove the importance of hard positive samples for contrastive learning and propose a hard positive sample pairs generation method (HPG) for conversation graphs, which can generate stronger gradient signals to improve the contrastive learning effect and make the model more robust. Experiments on various GNN encoders and datasets show that RDCL outperforms the current state-of-the-art models.
Curriculum Contrastive Learning for Fake News Detection
Authors: Jiachen Ma, Yong Liu, Meng Liu, Meng Han
Venue: CIKM
Year: 2022

Due to the rapid spread of fake news on social media, society and economy have been negatively affected in many ways. How to effectively identify fake news is a challenging problem that has received great attention from academic and industry. Existing deep learning methods for fake news detection require a large amount of labeled data to train the model, but obtaining labeled data is a time-consuming and labor-intensive process. To extract useful information from a large amount of unlabeled data, some contrastive learning methods for fake news detection are proposed. However, existing contrastive learning methods only randomly sample negative samples at different training stages, resulting in the role of negative samples not being fully played. Intuitively, increasing the contrastive difficulty of negative samples gradually in a way similar to human learning will contribute to improve the performance of the model. Inspired by the idea of curriculum learning, we propose a curriculum contrastive model (CCFD) for fake news detection which automatically select and train negative samples with different difficulty at different training stages. Furthermore, we also propose three new augmentation methods which consider the importance of edges and node attributes in the propagation structure to obtain more effective positive samples. The experimental results on three public datasets show that our model CCFD outperforms the existing state-of-the-art models for fake news detection.
NEST: Simulating Pandemic-like Events for Collaborative Filtering by Modeling User Needs Evolution
Authors: Chenglong Ma, Yongli Ren, Pablo Castells, Mark Sanderson
Venue: CIKM
Year: 2022

We outline a simulation-based study of the effect rapid population-scale concept drifts have on Collaborative Filtering (CF) models. We create a framework for analyzing the effects of macro-trends in population dynamics on the behavior of such models. Our framework characterizes population-scale concept drifts in item preferences and provides a lens to understand the influence events, such as a pandemic, have on CF models. Our experimental results show the initial impact on CF performance at the initial stage of such events, followed by an aggravated population herding effect during the event. The herding introduces a popularity bias that may benefit affected users, but which comes at the expense of a normal user experience. We propose an adaptive ensemble method that can effectively apply optimal algorithms to cope with the change brought about by different stages of the event.
DEMO: Disentangled Molecular Graph Generation via an Invertible Flow Model
Authors: Changsheng Ma, Qiang Yang, Xin Gao, Xiangliang Zhang
Venue: CIKM
Year: 2022

Molecular graph generation via deep generative models has attracted increasing attention. This is a challenging problem because it requires optimizing a given objective under a huge search space while obeying the chemical valence rules. Although recently developed molecular generation models have achieved promising results on generating novel, valid and unique molecules, few efforts have been made toward interpretable molecular graph generation. In this work, we propose DEMO, a flow-based model for DisEntangled Molecular graph generatiOn in a completely unsupervised manner, which is able to generate molecular graphs w.r.t. the learned disentangled latent factors that are relevant to molecular semantic features and interpretable structural patterns. Specifically, DEMO is composed of a VAE-encoder and a flow-generator. The VAE-encoder focuses on extracting global features of molecular graphs, and the flow-generator aims at disentangling these features to be corresponding to certain types of understandable molecular structure features while learning data distributions. To generate molecular graphs, DEMO simply runs the flow-generator in the reverse order due to the reversibility of the flow-based models. Extensive experimental results on two benchmark datasets demonstrate that DEMO outperforms the state-of-the-art methods in molecular generation, and takes the first step in interpretable molecular graph generation.
Robustness of Sketched Linear Classifiers to Adversarial Attacks
Authors: Ananth Mahadevan, Arpit Merchant, Yanhao Wang, Michael Mathioudakis
Venue: CIKM
Year: 2022

Linear classifiers are well-known to be vulnerable to adversarial attacks: they may predict incorrect labels for input data that are adversarially modified with small perturbations. However, this phenomenon has not been properly understood in the context of sketch-based linear classifiers, typically used in memory-constrained paradigms, which rely on random projections of the features for model compression. In this paper, we propose novel Fast-Gradient-Sign Method (FGSM) attacks for sketched classifiers in full, partial, and black-box information settings with regards to their internal parameters. We perform extensive experiments on the MNIST dataset to characterize their robustness as a function of perturbation budget. Our results suggest that, in the full-information setting, these classifiers are less accurate on unaltered input than their uncompressed counterparts but just as susceptible to adversarial attacks. But in more realistic partial and black-box information settings, sketching improves robustness while having lower memory footprint.
Locality Aware Temporal FMs for Crime Prediction
Authors: Sameen Mansha, Abdur Rehman, Shaaf Abdullah, Faisal Kamiran, Hongzhi Yin
Venue: CIKM
Year: 2022

Crime forecasting techniques can play a leading role in hindering crime occurrences, especially in areas under possible threat. In this paper, we propose Locality Aware Temporal Factorization Machines (LTFMs) for crime prediction. Its locality representation module deploys a spatial encoder to estimate the regional dependencies using Graph Convolutional Networks (GCNs). Then, the Point of Interest (POI) encoder computes the weighted attentive aggregation of location, crime, and POI latent representations. The dynamic crime representation module utilizes the transformer-based positional encodings to capture the dependencies among space, time, and crime categories. The encodings learnt from locality representation and crime category encoders, are projected into a factorization machine-based architecture via a shared feed-forward network. An extensive comparison with state-of-art techniques, using Chicago and New York's criminal records, shows the significance of LTFMs.
One Rating to Rule Them All?: Evidence of Multidimensionality in Human Assessment of Topic Labeling Quality
Authors: Amin Hosseiny Marani, Joshua Levine, Eric P. S. Baumer
Venue: CIKM
Year: 2022

Two general approaches are common for evaluating automatically generated labels in topic modeling: direct human assessment; or performance metrics that can be calculated without, but still correlate with, human assessment. However, both approaches implicitly assume that the quality of a topic label is single-dimensional. In contrast, this paper provides evidence that human assessments about the quality of topic labels consist of multiple latent dimensions. This evidence comes from human assessments of four simple labeling techniques. For each label, study participants responded to several items asking them to assess each label according to a variety of different criteria. Exploratory factor analysis shows that these human assessments of labeling quality have a two-factor latent structure. Subsequent analysis demonstrates that this multi-item, two-factor assessment can reveal nuances that would be missed using either a single-item human assessment of perceived label quality or established performance metrics. The paper concludes by suggesting future directions for the development of human-centered approaches to evaluating NLP and ML systems more broadly.
Not All Neighbors are Friendly: Learning to Choose Hop Features to Improve Node Classification
Authors: Sunil Kumar Maurya, Xin Liu, Tsuyoshi Murata
Venue: CIKM
Year: 2022

The fundamental operation of Graph Neural Networks (GNNs) is the feature aggregation step performed over neighbors of the node based on the structure of the graph. In addition to its own features, the node gets additional combined features from its neighbors for each hop. These aggregated features help define the similarity or dissimilarity of the nodes with respect to the labels and are useful for tasks like node classification. However, in real-world data, features of neighbors at different hops may not correlate with the node's features. Thus, any indiscriminate feature aggregation by GNN might cause the addition of noisy features leading to degradation in model's performance. In this work, we show that selective aggregation leads to better performance than default aggregation on the node classification task. Furthermore, we propose Dual-Net GNN architecture with a classifier model and a selector model. The classifier model trains over a subset of input node features to predict node labels while the selector model learns to provide optimal input subset to the classifier for best performance. These two models are trained jointly to learn the best subset of features that give higher accuracy in node label predictions. With extensive experiments, we show that our proposed model outperforms the state-of-the-art GNN models with remarkable improvements up to 27.8%.
The 2nd Workshop on Mixed-Initiative ConveRsatiOnal Systems (MICROS)
Authors: Ida Mele, Cristina Ioana Muntean, Mohammad Aliannejadi, Nikos Voskarides
Venue: CIKM
Year: 2022

The Mixed-Initiative ConveRsatiOnal Systems workshop (MICROS) aims at bringing novel ideas and investigating new solutions on conversational assistant systems. The increasing popularity of personal assistant systems, as well as smartphones, has changed the way users access online information, posing new challenges for information seeking and filtering. MICROS has a particular focus on mixed-initiative conversational systems, namely, systems that can provide answers in a proactive way (e.g., asking for clarification or proposing possible interpretations for ambiguous and vague requests). We invite people working on conversational systems or interested in the workshop topics to send us their position and research manuscripts.
Information Extraction from Social Media: A Hands-on Tutorial on Tasks, Data, and Open Source Tools
Authors: Shubhanshu Mishra, Rezvaneh Rezapour, Jana Diesner
Venue: CIKM
Year: 2022

Information extraction (IE) is a common sub-area of natural language processing that focuses on identifying structured data from unstructured data. One application domain of IE is Information Retrieval (IR), which relies on accurate and high-performance IE to retrieve high quality results from massive datasets. Another example of IE is to identify named entities in a text. For example, in the the sentence "Katy Perry lives in the USA", Katy Perry and USA are named entities of types of PERSON and LOCATION, respectively. Also, identify the sentiment expressed in a text is another instance of IE: in the sentence, "This movie was awesome", the expressed sentiment is positive. Finally, IE is concerned with identifying various linguistic aspects of text data, e.g., part of speech of words, noun phrases, dependency parses, etc., which can serve as features for additional IE tasks. This tutorial introduces participants to a) the usage of Python based, open-source tools that support IE from social media data (mainly Twitter), and b) best practices for ensuring the responsible use of IE and research data. Participants will learn and practice various lexical, semantic, and syntactic IE techniques that are commonly used for analyzing tweets. Participants will also be familiarized with the landscape of publicly available social media data (including popular NLP and IE benchmarks) and methods for collecting and preparing them for analysis. Furthermore, participants will be trained to use a suite of open source tools (SAIL for active learning, TwitterNER for named entity recognition, TweetNLP for transformer based NLP, and SocialMediaIE for multi task learning), which utilize advanced machine learning techniques (e.g., deep learning, active learning with human-in-the-loop, multi-lingual, and multi-task learning) to perform IE on their own or existing datasets. Participants will also learn how social contexts of text production and usage of results can be integrated into IE systems to improve these systems and to consider the role of time in improving social media IE quality. Finally, participants will learn about the governance of social media data for research purposes. The tools introduced in the tutorial will focus on the three main stages of IE, namely, collection of data (including annotation), data processing and analytics, and visualization of the extracted information. More details can be found at: https://socialmediaie.github.io/tutorials/
Efficient Compression Method for Roadside LiDAR Data
Authors: Md. Parvez Mollah, Biplob Debnath, Murugan Sankaradas, Srimat Chakradhar, Abdullah Mueen
Venue: CIKM
Year: 2022

Roadside LiDAR (Light Detection and Ranging) sensors are recently being explored for intelligent transportation systems aiming at safer and faster traffic management and vehicular operations. A key challenge in such systems is to efficiently transfer massive point-cloud data from the roadside LiDAR devices to the edge connected through a 5G network for real-time processing. In this paper, we consider the problem of compressing roadside (i.e. static) LiDAR data in real-time that provides a unique condition unexplored by current methods. Existing point-cloud compression methods assume moving LiDARs (that are mounted on vehicles) and do not exploit spatial consistency across frames over time. To this end, we develop a novel grouped wavelet technique for static roadside LiDAR data compression (i.e. SLiC). Our method compresses LiDAR data both spatially and temporally using a kd-tree data structure based on Haar wavelet coefficients. Experimental results show that SLiC can compress up to 1.9× more effectively than the state-of-the-art compression method can do. Moreover, SLiC is computationally more efficient to achieve 2× improvement in bandwidth usage over the best alternative. Even with this impressive gain in communication and storage efficiency, SLiC retains down-the-pipeline application's accuracy.
MEMENTO: Neural Model for Estimating Individual Treatment Effects for Multiple Treatments
Authors: Abhirup Mondal, Anirban Majumder, Vineet Chaoji
Venue: CIKM
Year: 2022

Learning individual level treatment effects from observational data is a problem of growing interest. For instance, inferring the effect of delivery promises on purchase of products on an e-commerce site or selecting the most effective treatment for a specific patient. Although the scenarios where we want to estimate the treatment effects in presence of multiple treatments is quite common in real life, most existing works related to individual treatment effect (ITE) are focused primarily on binary treatments and do not have a natural extension to the multi-treatment scenarios. In this paper we present MEMENTO ? a methodology and a framework to estimate individual treatment effect for multi-treatment scenarios, where the treatments are discrete and finite. Our approach is based on obtaining matching representations of the confounders for the various treatment types. This is achieved through minimization of an upper bound on the sum of factual and counterfactual losses. Experiments on real and semi-synthetic datasets show that MEMENTO is able to outperform known techniques for multi-treatment scenarios by close to 10% in certain use-cases. The proposed framework has been deployed for the problem of identifying minimum order quantity of a product in Amazon in an emerging marketplace and has re- sulted in a 4.7% reduction in shipping costs as proved from an A/B experiment.
Music4All-Onion - A Large-Scale Multi-faceted Content-Centric Music Recommendation Dataset
Authors: Marta Moscati, Emilia Parada-Cabaleiro, Yashar Deldjoo, Eva Zangerle, Markus Schedl
Venue: CIKM
Year: 2022

When we appreciate a piece of music, it is most naturally because of its content, including rhythmic, tonal, and timbral elements as well as its lyrics and semantics. This suggests that the human affinity for music is inherently content-driven. This kind of information is, however, still frequently neglected by mainstream recommendation models based on collaborative filtering that rely solely on user-item interactions to recommend items to users. A major reason for this neglect is the lack of standardized datasets that provide both collaborative and content information. The work at hand addresses this shortcoming by introducing Music4All-Onion, a large-scale, multi-modal music dataset. The dataset expands the Music4All dataset by including 26 additional audio, video, and metadata characteristics for 109,269 music pieces. In addition, it provides a set of 252,984,396 listening records of 119,140 users, extracted from the online music platform Last.fm, which allows leveraging user-item interactions as well. We organize distinct item content features in an onion model according to their semantics, and perform a comprehensive examination of the impact of different layers of this model (e.g., audio features, user-generated content, and derivative content) on content-driven music recommendation, demonstrating how various content features influence accuracy, novelty, and fairness of music recommendation systems. In summary, with Music4All-Onion, we seek to bridge the gap between collaborative filtering music recommender systems and content-centric music recommendation requirements.
Network Aware Forecasting for eCommerce Supply Planning
Authors: K. V. M. Naidu, Praveen Gupta, Vaishnavi Gujjula
Venue: CIKM
Year: 2022

A real world supply chain planning starts with the demand forecasting as a key input. In most scenarios, especially in fields like e-commerce where demand patterns are complex and are large scale, demand forecasting is done independent of supply chain constraints. There have been a plethora of methods, old and recent, for generating accurate forecasts. However, to the best of our knowledge, none of the methods take supply chain constraints into account during forecasting. In this paper, we are primarily interested in supply chain aware forecasting methods that does not impose any restrictions on demand forecasting process. We assume that the base forecasts follow a distribution from exponential family and are provided as input to supply chain planning by specifying the distribution form and parameters. With this in mind, following are the contributions of our paper. First, we formulate the supply chain aware forecast improvement of a base forecast as finding the game theoretically optimal parameters satisfying the supply chain constraints. Second, for regular distributions from exponential family, we show that this translates to projecting base forecast onto the (convex) set defined by supply constraints, which is at least as accurate as the base forecasts. Third, we note that using off the shelf convex solvers does not scale for large instances of supply chain, which is typical in e-commerce settings. We propose algorithms that scale better with problem size. We propose a general gradient descent based approach that works across different distributions from exponential family. We also propose a network flow based exact algorithm for Laplace distribution (which relates to mean absolute error, which is the most commonly used metric in forecasting). Finally, we substantiate the theoretical results with extensive experiments on a real life e-commerce data set as well as a range of synthetic data sets.
Expressions Causing Differences in Emotion Recognition in Social Networking Service Documents
Authors: Tsubasa Nakagawa, Shunsuke Kitada, Hitoshi Iyatomi
Venue: CIKM
Year: 2022

It is often difficult to correctly infer a writer's emotion from text exchanged online, and differences in recognition between writers and readers can be problematic. In this paper, we propose a new framework for detecting sentences that create differences in emotion recognition between the writer and the reader and for detecting the kinds of expressions that cause such differences. The proposed framework consists of a bidirectional encoder representations from transformers (BERT)-based detector that detects sentences causing differences in emotion recognition and an analysis that acquires expressions that characteristically appear in such sentences. The detector, based on a Japanese SNS-document dataset with emotion labels annotated by both the writer and three readers of the social networking service (SNS) documents, detected "hidden-anger sentences" with AUC = 0.772; these sentences gave rise to differences in the recognition of anger. Because SNS documents contain many sentences whose meaning is extremely difficult to interpret, by analyzing the sentences detected by this detector, we obtained several expressions that appear characteristically in hidden-anger sentences. The detected sentences and expressions do not convey anger explicitly, and it is difficult to infer the writer's anger, but if the implicit anger is pointed out, it becomes possible to guess why the writer is angry. Put into practical use, this framework would likely have the ability to mitigate problems based on misunderstandings.