OpenRaiser/Intern-Atlas huggingface dataset:
Political discourse on social media: Echo chambers, gatekeepers, and the price of bipartisanship
Authors: Kiran Garimella, Gianmarco De Francisci Morales, Aristides Gionis, Michael Mathioudakis
Venue: Conference WWW 2018
Year: 2018

Echo chambers, i.e., situations where one is exposed only to opinions that agree with their own, are an increasing concern for the political discourse in many democratic countries. This paper studies the phenomenon of political echo chambers on social media. We identify the two components in the phenomenon: the opinion that is shared, and the »chamber» (i.e., the social network) that allows the opinion to »echo» (i.e., be re-shared in the network) -- and examine closely at how these two components interact. We define a production and consumption measure for social-media users, which captures the political leaning of the content shared and received by them. By comparing the two, we find that Twitter users are, to a large degree, exposed to political opinions that agree with their own. We also find that users who try to bridge the echo chambers, by sharing content with diverse leaning, have to pay a »price of bipartisanship» in terms of their network centrality and content appreciation. In addition, we study the role of »gatekeepers,» users who consume content with diverse leaning but produce partisan content (with a single-sided leaning), in the formation of echo chambers. Finally, we apply these findings to the task of predicting partisans and gatekeepers from social and content features. While partisan users turn out relatively easy to identify, gatekeepers prove to be more challenging.
FlowQA: Grasping flow in history for conversational machine comprehension
Authors: Hsin-Yuan Huang, Eunsol Choi, Wen-tau Yih
Venue: Conference ICLR 2019
Year: 2019

Conversational machine comprehension requires the understanding of the conversation history, such as previous question/answer pairs, the document context, and the current question. To enable traditional, single-turn models to encode the history comprehensively, we introduce Flow, a mechanism that can incorporate intermediate representations generated during the process of answering previous questions, through an alternating parallel processing structure. Compared to approaches that concatenate previous questions/answers as input, Flow integrates the latent semantics of the conversation history more deeply. Our model, FlowQA, shows superior performance on two recently proposed conversational challenges (+7.2% F1 on CoQA and +4.0% on QuAC). The effectiveness of Flow also shows in other tasks. By reducing sequential instruction understanding to conversational machine comprehension, FlowQA outperforms the best models on all three domains in SCONE, with +1.8% to +4.4% improvement in accuracy.
Neighbor interaction aware graph convolution networks for recommendation
Authors: Jianing Sun, Yingxue Zhang 0001, Wei Guo 0006, Huifeng Guo, Ruiming Tang, Xiuqiang He 0001, Chen Ma 0001, Mark Coates
Venue: Conference SIGIR 2020
Year: 2020

Personalized recommendation plays an important role in many online services. Substantial research has been dedicated to learning embeddings of users and items to predict a user's preference for an item based on the similarity of the representations. In many settings, there is abundant relationship information, including user-item interaction history, user-user and item-item similarities. In an attempt to exploit these relationships to learn better embeddings, researchers have turned to the emerging field of Graph Convolutional Neural Networks (GCNs), and applied GCNs for recommendation. Although these prior works have demonstrated promising performance, directly apply GCNs to process the user-item bipartite graph is suboptimal because the GCNs do not consider the intrinsic differences between user nodes and item nodes. Additionally, existing large-scale graph neural networks use aggregation functions such as sum/mean/max pooling operations to generate a node embedding that considers the nodes' neighborhood (i.e., the adjacent nodes in the graph), and these simple aggregation strategies fail to preserve the relational information in the neighborhood. To resolve the above limitations, in this paper, we propose a novel framework NIA-GCN, which can explicitly model the relational information between neighbor nodes and exploit the heterogeneous nature of the user-item bipartite graph. We conduct empirical studies on four public benchmarks, demonstrating a significant improvement over state-of-the-art approaches. Furthermore, we generalize our framework to a commercial App store recommendation scenario. We observe significant improvement on a large-scale commercial dataset, demonstrating the practical potential for our proposed solution as a key component of a large scale commercial recommender system. Furthermore, online experiments are conducted to demonstrate that NIA-GCN outperforms the baseline by 10.19% and 9.95% in average in terms of CTR and CVR during ten-day AB test in a mainstream App store.
Bars: Towards open benchmarking for recommender systems
Authors: Jieming Zhu, Quanyu Dai, Liangcai Su, Rong Ma, Jinyang Liu, Guohao Cai, Xi Xiao, Rui Zhang
Venue: Conference SIGIR 2022
Year: 2022

The past two decades have witnessed the rapid development of personalized recommendation techniques. Despite significant progress made in both research and practice of recommender systems, to date, there is a lack of a widely-recognized benchmarking standard in this field. Many existing studies perform model evaluations and comparisons in an ad-hoc manner, for example, by employing their own private data splits or using different experimental settings. Such conventions not only increase the difficulty in reproducing existing studies, but also lead to inconsistent experimental results among them. This largely limits the credibility and practical value of research results in this field. To tackle these issues, we present an initiative project (namely BARS) aiming for open benchmarking for recommender systems. In comparison to some earlier attempts towards this goal, we take a further step by setting up a standardized benchmarking pipeline for reproducible research, which integrates all the details about datasets, source code, hyper-parameter settings, running logs, and evaluation results. The benchmark is designed with comprehensiveness and sustainability in mind. It covers both matching and ranking tasks, and also enables researchers to easily follow and contribute to the research in this field. This project will not only reduce the redundant efforts of researchers to re-implement or re-run existing baselines, but also drive more solid and reproducible research on recommender systems. We would like to call upon everyone to use the BARS benchmark for future evaluation, and contribute to the project through the portal at: https://openbenchmark.github.io/BARS.
CS-MLGCN: Multiplex graph convolutional networks for community search in multiplex networks
Authors: Ali Behrouz, Farnoosh Hashemi
Venue: Conference CIKM 2022
Year: 2022

Community Search (CS) is one of the fundamental tasks in network science and has attracted much attention due to its ability to discover personalized communities with a wide range of applications. Given any query nodes, CS seeks to find a densely connected subgraph containing query nodes. Most existing approaches usually study networks with a single type of proximity between nodes, which defines a single view of a network. However, in many applications such as biological, social, and transportation networks, interactions between objects span multiple aspects, yielding networks with multiple views, called multiplex networks. Existing CS approaches in multiplex networks adopt pre-defined subgraph patterns to model the communities, which cannot find communities that do not have such pre-defined patterns in real-world networks. In this paper, we propose a query-driven graph convolutional network in multiplex networks, CS-MLGCN, that can capture flexible community structures by learning from the ground-truth communities in a data-driven fashion. CS-MLGCN first combines the local query-dependent structure and global graph embedding in each type of proximity and then uses an attention mechanism to incorporate information on different types of relations. Experiments on real-world graphs with ground-truth communities validate the quality of the solutions we obtain and the efficiency of our model.
Logmine: Fast pattern recognition for log analytics
Authors: Hossein Hamooni, Biplob Debnath, Jianwu Xu, Hui Zhang, Guofei Jiang, Abdullah Mueen
Venue: Conference CIKM 2016
Year: 2016

Modern engineering incorporates smart technologies in all aspects of our lives. Smart technologies are generating terabytes of log messages every day to report their status. It is crucial to analyze these log messages and present usable information (e.g. patterns) to administrators, so that they can manage and monitor these technologies. Patterns minimally represent large groups of log messages and enable the administrators to do further analysis, such as anomaly detection and event prediction. Although patterns exist commonly in automated log messages, recognizing them in massive set of log messages from heterogeneous sources without any prior information is a significant undertaking. We propose a method, named LogMine, that extracts high quality patterns for a given set of log messages. Our method is fast, memory efficient, accurate, and scalable. LogMine is implemented in map-reduce framework for distributed platforms to process millions of log messages in seconds. LogMine is a robust method that works for heterogeneous log messages generated in a wide variety of systems. Our method exploits algorithmic techniques to minimize the computational overhead based on the fact that log messages are always automatically generated. We evaluate the performance of LogMine on massive sets of log messages generated in industrial applications. LogMine has successfully generated patterns which are as good as the patterns generated by exact and unscalable method, while achieving a 500× speedup. Finally, we describe three applications of the patterns generated by LogMine in monitoring large scale industrial systems.
Clustering event logs using iterative partitioning
Authors: Adetokunbo Makanju, Nur Zincir-Heywood, Evangelos E. Milios
Venue: Conference KDD 2009
Year: 2009

The importance of event logs, as a source of information in systems and network management cannot be overemphasized. With the ever increasing size and complexity of today's event logs, the task of analyzing event logs has become cumbersome to carry out manually. For this reason recent research has focused on the automatic analysis of these log files. In this paper we present IPLoM (Iterative Partitioning Log Mining), a novel algorithm for the mining of clusters from event logs. Through a 3-Step hierarchical partitioning process IPLoM partitions log data into its respective clusters. In its 4th and final stage IPLoM produces cluster descriptions or line formats for each of the clusters produced. Unlike other similar algorithms IPLoM is not based on the Apriori algorithm and it is able to find clusters in data whether or not its instances appear frequently. Evaluations show that IPLoM outperforms the other algorithms statistically significantly, and it is also able to achieve an average F-Measure performance 78% when the closest other algorithm achieves an F-Measure performance of 10%.
Disinformation Warfare: Understanding State-Sponsored Trolls on Twitter and Their Influence on the Web
Authors: Savvas Zannettou, Tristan Caulfield, Emiliano De Cristofaro, Michael Sirivianos, Gianluca Stringhini, Jeremy Blackburn
Venue: Conference WWW 2019
Year: 2019

Over the past couple of years, anecdotal evidence has emerged linking coordinated campaigns by state-sponsored actors with efforts to manipulate public opinion on the Web, often around major political events, through dedicated accounts, or “trolls.” Although they are often involved in spreading disinformation on social media, there is little understanding of how these trolls operate, what type of content they disseminate, and most importantly their influence on the information ecosystem. In this paper, we shed light on these questions by analyzing 27K tweets posted by 1K Twitter users identified as having ties with Russia’s Internet Research Agency and thus likely state-sponsored trolls. We compare their behavior to a random set of Twitter users, finding interesting differences in terms of the content they disseminate, the evolution of their account, as well as their general behavior and use of Twitter. Then, using Hawkes Processes, we quantify the influence that trolls had on the dissemination of news on social platforms like Twitter, Reddit, and 4chan. Overall, our findings indicate that Russian trolls managed to stay active for long periods of time and to reach a substantial number of Twitter users with their tweets. When looking at their ability of spreading news content and making it viral, however, we find that their effect on social platforms was minor, with the significant exception of news published by the Russian state-sponsored news outlet RT (Russia Today).
A semi-supervised approach to modeling web search satisfaction
Authors: Ahmed Hassan
Venue: Conference SIGIR 2012
Year: 2012

Web search is an interactive process that involves actions from Web search users and responses from the search engine. Many research efforts have been made to address the problem of understanding search behavior in general. Some of this work focused on predicting whether a particular user has succeeded in achieving her search goal or not. Most of these studies have faced the problem of the lack of reliable labeled data to learn from. Unlike labeled data, unlabeled data recording behavioral signals in Web search is widely available in search logs. In this work, we study the plausibility of using labeled and unlabeled data to learn better models of user behavior that can be used to predict search success more effectively. We present a semi-supervised approach to modeling Web search satisfaction. The proposed approach can use either labeled data only or both labeled and unlabeled data. We show that the proposed model outperforms previous methods for modeling search success using labeled data. We also show that adding unlabeled data improves the effectiveness of the proposed models and that the proposed method outperforms other strong semi-supervised baselines.
CoPE: Modeling Continuous Propagation and Evolution on Interaction Graph
Authors: Yao Zhang, Yun Xiong, Dongsheng Li, Caihua Shan, Kan Ren, Yangyong Zhu
Venue: Conference CIKM 2021
Year: 2021

Human interactions with items are being constantly logged, which enables advanced representation learning and facilitates various tasks. Instead of generating static embeddings at the end of training, several temporal embedding methods were recently proposed to learn user and item embeddings as functions of time, where each entity has a trajectory of embedding vectors aiming to encode the full dynamics. However, these methods may not be optimal to encode the dynamical behaviors on the interaction graphs in that they can not generate "fully''-temporal embeddings and do not consider information propagation. In this paper, we tackle the issues and propose CoPE (Co ntinuous P ropagation and E volution). We use an ordinary differential equation based graph neural network to model information propagation and more sophisticated evolution patterns. We train CoPE on sequences of interactions with the help of meta-learning to ensure fast adaptation to the most recent interactions. We evaluate CoPE on three tasks and prove its effectiveness.
Communication Efficient Federated Generalized Tensor Factorization for Collaborative Health Data Analytics
Authors: Jing Ma, Qiuchen Zhang, Jian Lou, Li Xiong, Joyce C. Ho
Venue: Conference WWW 2021
Year: 2021

Modern healthcare systems knitted by a web of entities (e.g., hospitals, clinics, pharmacy companies) are collecting a huge volume of healthcare data from a large number of individuals with various medical procedures, medications, diagnosis, and lab tests. To extract meaningful medical concepts (i.e., phenotypes) from such higher-arity relational healthcare data, tensor factorization has been proven to be an effective approach and received increasing research attention, due to their intrinsic capability to represent the high-dimensional data. Recently, federated learning offers a privacy-preserving paradigm for collaborative learning among different entities, which seemingly provides an ideal potential to further enhance the tensor factorization-based collaborative phenotyping to handle sensitive personal health data. However, existing attempts to federated tensor factorization come with various limitations, including restrictions to the classic tensor factorization, high communication cost and reduced accuracy. We propose a communication efficient federated generalized tensor factorization, which is flexible enough to choose from a variate of losses to best suit different types of data in practice. We design a three-level communication reduction strategy tailored to the generalized tensor factorization, which is able to reduce the uplink communication cost up to 99.90%. In addition, we theoretically prove that our algorithm does not compromise convergence speed despite the aggressive communication compression. Extensive experiments on two real-world electronics health record datasets demonstrate the efficiency improvements in terms of computation and communication cost.
Fostering Engagement of Underserved Communities with Credible Health Information on Social Media
Authors: Agha Ali Raza, Mustafa Naseem, Namoos Hayat Qasmi, Shan Randhawa, Fizzah Malik, Behzad Taimur, Sacha St-Onge Ahmad, Sarojini Hirshleifer, Arman Rezaee, Aditya Vashistha
Venue: Conference WWW 2022
Year: 2022

The COVID-19 pandemic has necessitated rapid top-down dissemination of reliable and actionable information. This presents unique challenges in engaging low-literate communities that live in poverty and lack access to the Internet. We describe the design and deployment of a voice-based social media platform, accessible over simple phones, for actively engaging such communities in Pakistan with reliable COVID information. We developed three strategies to overcome users’ hesitation, mistrust, and skepticism in engaging with COVID content. Users were: (1) encouraged to listen to reliable COVID advisory, (2) incentivized to share authentic content with others, and (3) prompted to critically think about COVID-related information behaviors. Using a mixed-methods evaluation, we show that users approached with all three strategies had a significantly higher engagement with COVID content compared to others. We discuss how new designs of social media can enable users to engage with and propagate authentic information.
Offline Evaluation to Make Decisions About PlaylistRecommendation Algorithms
Authors: Alois Gruson, Praveen Chandar, Christophe Charbuillet, James McInerney, Samantha Hansen, Damien Tardieu, Ben Carterette
Venue: Conference WSDM 2019
Year: 2019

Evaluating algorithmic recommendations is an important, but difficult, problem. Evaluations conducted offline using data collected from user interactions with an online system often suffer from biases arising from the user interface or the recommendation engine. Online evaluation (A/B testing) can more easily address problems of bias, but depending on setting can be time-consuming and incur risk of negatively impacting the user experience, not to mention that it is generally more difficult when access to a large user base is not taken as granted. A compromise based on \em counterfactual analysis is to present some subset of online users with recommendation results that have been randomized or otherwise manipulated, log their interactions, and then use those to de-bias offline evaluations on historical data. However, previous work does not offer clear conclusions on how well such methods correlate with and are able to predict the results of online A/B tests. Understanding this is crucial to widespread adoption of new offline evaluation techniques in recommender systems. In this work we present a comparison of offline and online evaluation results for a particular recommendation problem: recommending playlists of tracks to a user looking for music. We describe two different ways to think about de-biasing offline collections for more accurate evaluation. Our results show that, contrary to much of the previous work on this topic, properly-conducted offline experiments do correlate well to A/B test results, and moreover that we can expect an offline evaluation to identify the best candidate systems for online testing with high probability.
Polarized graph neural networks
Authors: Zheng Fang, Lingjun Xu, Guojie Song, Qingqing Long, Yingxue Zhang
Venue: Conference WWW 2022
Year: 2022

Despite the recent success of Message-passing Graph Neural Networks (MP-GNNs), the strong inductive bias of homophily limits their ability to generalize to heterophilic graphs and leads to the over-smoothing problem. Most existing works attempt to mitigate this issue in the spirit of emphasizing the contribution from similar neighbors and reducing those from dissimilar ones when performing aggregation, where the dissimilarities are utilized passively and their positive effects are ignored, leading to suboptimal performances. Inspired by the idea of attitude polarization in social psychology, that people tend to be more extreme when exposed to an opposite opinion, we propose Polarized Graph Neural Network (Polar-GNN). Specifically, pairwise similarities and dissimilarities of nodes are firstly modeled with node features and topological structure information. And specially, we assign negative weights for those dissimilar ones. Then nodes aggregate the messages on a hyper-sphere through a polarization operation, which effectively exploits both similarities and dissimilarities. Furthermore, we theoretically demonstrate the validity of the proposed operation. Lastly, an elaborately designed loss function is introduced for the hyper-spherical embedding space. Extensive experiments on real-world datasets verify the effectiveness of our model.
Asa: Adversary situation awareness via heterogeneous graph convolutional networks
Authors: Rui Wen, Jianyu Wang, Chunming Wu, Jian Xiong
Venue: Conference WWW 2020
Year: 2020

Given a large graph with millions of vertices and different types, how can we spot anomalies and find potential adversaries in time? Most graph-based fraud detection algorithms focus on finding dense blocks, discovering local subgraphs, designing belief propagation, and latent factor models. However, as fraudsters in online social networks gradually become adversarial, distributed, and invisible, it is easy for them to evade traditional detection methods. Even worse, existing detection systems are fragile to the new attacks. Once attackers update their tragedies, they will be inefficient. Our focus is to detect potential adversaries as soon as possible. We design and propose ASA (Adversary Situation Awareness), a system that (a) uncovers potential adversaries in time, (b) utilizes heterogeneous information in the graph, and (c)is effective in real-world data. We do experiments on a heterogeneous graph including 198,541 nodes with five types and 224,384 edges. The result shows that ASA can spot potential adversaries and successfully recovers 60 of potential abnormal users within a few minutes. Additionally, after two months we deployed it, ASA could achieve 90 recall, which means ASA can well discover a small number of adversaries with a latent period in time.