OpenRaiser/Intern-Atlas huggingface dataset:
Make It Easy: An Effective End-to-End Entity Alignment Framework
Authors: Congcong Ge, Xiaoze Liu, Lu Chen 0001, Baihua Zheng, Yunjun Gao
Venue: Conference SIGIR 2021
Year: 2021

Entity alignment (EA) is a prerequisite for enlarging the coverage of a unified knowledge graph. Previous EA approaches either restrain the performance due to inadequate information utilization or need labor-intensive pre-processing to get external or reliable information to perform the EA task. This paper proposes EASY, an effective end-to-end EA framework, which is able to (i) remove the labor-intensive pre-processing by fully discovering the name information provided by the entities themselves; and (ii) jointly fuse the features captured by the names of entities and the structural information of the graph to improve the EA results. Specifically, EASY first introduces NEAP, a highly effective name-based entity alignment procedure, to obtain an initial alignment that has reasonable accuracy and meanwhile does not require much memory consumption or any complex training process. Then, EASY invokes SRS, a novel structure-based refinement strategy, to iteratively correct the misaligned entities generated by NEAP to further enhance the entity alignment. Extensive experiments demonstrate the superiority of our proposed EASY with significant improvement against 13 existing state-of-the-art competitors.
Writing style matters: An examination of bias and fairness in information retrieval systems
Authors: Hongliu Cao
Venue: Conference WSDM 2025
Year: 2025

The rapid advancement of Language Model technologies has opened new opportunities, but also introduced new challenges related to bias and fairness. This paper explores the uncharted territory of potential biases in state-of-the-art universal text embedding models towards specific document and query writing styles within Information Retrieval (IR) systems. Our investigation reveals that different embedding models exhibit different preferences for document writing style, while more informal and emotive styles are less favored by most embedding models. In terms of query writing styles, many embedding models tend to match the query style with the retrieved document style, but some show a consistent preference for specific styles. Text embedding models fine-tuned on synthetic data generated by LLMs display a consistent preference for certain style of generated data. These biases in text embedding based IR systems can inadvertently silence or marginalize certain communication styles, thereby posing a significant threat to fairness in information retrieval. Finally, we also compare the answer styles of Retrieval Augmented Generation (RAG) systems based on different LLMs and find out that most text embedding models are biased towards LLM's answer styles when used as evaluation metrics for answer correctness. This study sheds light on the critical issue of writing style based bias in IR systems, offering valuable insights for the development of more fair and robust models.
Learning to combine discriminative classifiers: confidence based
Authors: Chi-Hoon Lee
Venue: Conference KDD 2010
Year: 2010

Much of research in data mining and machine learning has led to numerous practical applications. Spam filtering, fraud detection, and user query-intent analysis has relied heavily on machine learned classifiers, and resulted in improvements in robust classification accuracy. Combining multiple classifiers (a.k.a. Ensemble Learning) is a well studied and has been known to improve effectiveness of a classifier. To address two key challenges in Ensemble Learning-- (1) learning weights of individual classifiers and (2) the combination rule of their weighted responses, this paper proposes a novel Ensemble classifier, EnLR, that computes weights of responses from discriminative classifiers and combines their weighted responses to produce a single response for a test instance. The combination rule is based on aggregating weighted responses, where a weight of an individual classifier is inversely based on their respective variances around their responses. Here, variance quantifies the uncertainty of the discriminative classifiers' parameters, which in turn depends on the training samples. As opposed to other ensemble methods where the weight of each individual classifier is learned as a part of parameter learning and thus the same weight is applied to all testing instances, our model is actively adjusted as individual classifiers become confident at its decision for a test instance. Our empirical experiments on various data sets demonstrate that our combined classifier produces "effective" results when compared with a single classifier. Our novel classifier shows statistically significant better accuracy when compared to well known Ensemble methods -- Bagging and AdaBoost. In addition to robust accuracy, our model is extremely efficient dealing with high volumes of training samples due to the independent learning paradigm among its multiple classifiers. It is simple to implement in a distributed computing environment such as Hadoop.
When llm meets hypergraph: A sociological analysis on personality via online social networks
Authors: Zhiyao Shu, Xiangguo Sun, Hong Cheng
Venue: Conference CIKM 2024
Year: 2024

Individual personalities significantly influence our perceptions, decisions, and social interactions, which is particularly crucial for gaining insights into human behavior patterns in online social network analysis. Many psychological studies have observed that personalities are strongly reflected in their social behaviors and social environments. Unfortunately, psychological traits like one's personality are high-level and hidden in the innermost corner of data, which is intractable to be uncovered by traditional data mining approaches; The data quality of online social networks is far from sufficient to support such profound psychological analysis, because user behavior records and their attributes are usually very fragmented, missing lots of key information to understand a person in depth; In addition, the social environments in online networks are very complicated, making the interaction patterns between users and their environments underexplored. In light of these problems, this paper proposes a sociological analysis framework for one's personality in an environment-based view instead of individual-level data mining. Specifically, to comprehensively understand an individual's behavior from low-quality records, we leverage the powerful associative ability of LLMs by designing an effective prompt. In this way, LLMs can integrate various scattered information with their external knowledge to generate higher-quality profiles, which can significantly improve the personality analysis performance. To explore the interactive mechanism behind the users and their online environments, we design an effective hypergraph neural network where the hypergraph nodes are users and the hyperedges in the hypergraph are social environments. We offer a useful dataset with user profile data, personality traits, and several detected environments from the real-world social platform. To the best of our knowledge, this is the first network-based dataset containing both hypergraph structure and social information, which could push forward future research in this area further. By employing the framework on this dataset, we can effectively capture the nuances of individual personalities and their online behaviors, leading to a deeper understanding of human interactions in the digital world. Our code and dataset are open accessible.
Sciharvester: Searching scientific documents for numerical values
Authors: Maciej Rybinski, Stephen Wan, Sarvnaz Karimi, Cécile Paris, Brian Jin, Neil I. Huth, Peter J. Thorburn, Dean P. Holzworth
Venue: Conference SIGIR 2023
Year: 2023

A challenge for search technologies is to support scientific literature surveys that present overviews of the reported numerical values documented for specific physical properties. We present SciHarvester, a system tailored to address this problem for agronomic science. It provides an interface to search PubAg documents, allowing complex queries involving restrictions on numerical values. SciHarvester identifies relevant documents and generates overview of reported parameter values. The system allows interrogation of the results to explain the system's performance. Our evaluations demonstrate the promise of incorporating information extraction techniques with the use of neural scoring mechanisms.
Auralist: Introducing Serendipity into Music Recommendation
Authors: Yuan Cao Zhang, Diarmuid Ó Séaghdha, Daniele Quercia, Tamas Jambor
Venue: Conference WSDM 2012
Year: 2012

Recommendation systems exist to help users discover content in a large body of items. An ideal recommendation system should mimic the actions of a trusted friend or expert, producing a personalised collection of recommendations that balance between the desired goals of accuracy, diversity, novelty and serendipity. We introduce the Auralist recommendation framework, a system that - in contrast to previous work - attempts to balance and improve all four factors simultaneously. Using a collection of novel algorithms inspired by principles of "serendipitous discovery", we demonstrate a method of successfully injecting serendipity, novelty and diversity into recommendations whilst limiting the impact on accuracy. We evaluate Auralist quantitatively over a broad set of metrics and, with a user study on music recommendation, show that Auralist's emphasis on serendipity indeed improves user satisfaction.
The dynamics of (not) unfollowing misinformation spreaders
Authors: Joshua Ashkinaze, Eric Gilbert, Ceren Budak
Venue: Conference WWW 2024
Year: 2024

Many studies explore how people "come into" misinformation exposure. But much less is known about how people "come out of" misinformation exposure. Do people organically sever ties to misinformation spreaders? And what predicts doing so? Over six months, we tracked the frequency and predictors of ~900K followers unfollowing ~5K health misinformation spreaders on Twitter. We found that misinformation ties are persistent. Monthly unfollowing rates are just 0.52%. In other words, 99.5% of misinformation ties persist each month. Users are also 31% more likely to unfollownon- misinformation spreaders than they are to unfollow misinformation spreaders. Although generally infrequent, the factors most associated with unfollowing misinformation spreaders are (1) redundancy and (2) ideology. First, users initially following many spreaders, or who follow spreaders that tweet often, are most likely to unfollow later. Second, liberals are more likely to unfollow than conservatives. Overall, we observe a strong persistence of misinformation ties. The fact that users rarely unfollow misinformation spreaders suggests a need for external nudges and the importance of preventing exposure from arising in the first place.
LMbot: distilling graph knowledge into language model for graph-less deployment in twitter bot detection
Authors: Zijian Cai, Zhaoxuan Tan, Zhenyu Lei, Zifeng Zhu, Hongrui Wang, Qinghua Zheng, Minnan Luo
Venue: Conference WSDM 2024
Year: 2024

As malicious actors employ increasingly advanced and widespread bots to disseminate misinformation and manipulate public opinion, the detection of Twitter bots has become a crucial task. Though graph-based Twitter bot detection methods achieve state-of-the-art performance, we find that their inference depends on the neighbor users multi-hop away from the targets, and fetching neighbors is time-consuming and may introduce sampling bias. At the same time, our experiments reveal that after finetuning on Twitter bot detection task, pretrained language models achieve competitive performance while do not require a graph structure during deployment. Inspired by this finding, we propose a novel bot detection framework LMBot that distills the graph knowledge into language models (LMs) for graph-less deployment in Twitter bot detection to combat data dependency challenge. Moreover, LMBot is compatible with graph-based and graph-less datasets. Specifically, we first represent each user as a textual sequence and feed them into the LM for domain adaptation. For graph-based datasets, the output of LM serves as input features for the GNN, enabling LMBot to optimize for bot detection and distill knowledge back to the LM in an iterative, mutually enhancing process. Armed with the LM, we can perform graph-less inference with graph knowledge, which resolves the graph data dependency and sampling bias issues. For datasets without graph structure, we simply replace the GNN with an MLP, which also shows strong performance. Our experiments demonstrate that LMBot achieves state-of-the-art performance on four Twitter bot detection benchmarks. Extensive studies also show that LMBot is more robust, versatile, and efficient compared to existing graph-based Twitter bot detection methods.
Simplistic collection and labeling practices limit the utility of benchmark datasets for twitter bot detection
Authors: Chris Hays, Zachary Schutzman, Manish Raghavan, Erin Walk, Philipp Zimmer
Venue: Conference WWW 2023
Year: 2023

Accurate bot detection is necessary for the safety and integrity of online platforms. It is also crucial for research on the influence of bots in elections, the spread of misinformation, and financial market manipulation. Platforms deploy infrastructure to flag or remove automated accounts, but their tools and data are not publicly available. Thus, the public must rely on third-party bot detection. These tools employ machine learning and often achieve near perfect performance for classification on existing datasets, suggesting bot detection is accurate, reliable and fit for use in downstream applications. We provide evidence that this is not the case and show that high performance is attributable to limitations in dataset collection and labeling rather than sophistication of the tools. Specifically, we show that simple decision rules -- shallow decision trees trained on a small number of features -- achieve near-state-of-the-art performance on most available datasets and that bot detection datasets, even when combined together, do not generalize well to out-of-sample datasets. Our findings reveal that predictions are highly dependent on each dataset's collection and labeling procedures rather than fundamental differences between bots and humans. These results have important implications for both transparency in sampling and labeling procedures and potential biases in research using existing bot detection tools for pre-processing.
Dual graph networks with synthetic oversampling for imbalanced rumor detection on social media
Authors: Yen-Wen Lu, Chih-Yao Chen, Cheng-Te Li
Venue: Conference WWW 2024
Year: 2024

Rumor detection is to identify and mitigate potentially damaging falsehoods, thereby shielding the public from misleading information. However, existing methods fall short of tackling class imbalance, meaning rumor is less common than true messages, as they lack specific adaptation for the context of rumor dissemination. In this work, we propose Dual Graph Networks with Synthetic Oversampling (SynDGN), a novel method that can determine whether a claim made on social media is rumor or not in the presence of class imbalance. SynDGN properly utilizes dual graphs to integrate social media contexts and user characteristics to make accurate predictions. Experiments conducted on two well-known datasets verify that SynDGN consistently outperforms state-of-the-art models, regardless of whether the data is balanced or not.
Gpt-generated text detection: Benchmark dataset and tensor-based detection method
Authors: Zubair Qazi, William Shiao, Evangelos E. Papalexakis
Venue: Conference WWW 2024
Year: 2024

As natural language models like ChatGPT become increasingly prevalent in applications and services, the need for robust and accurate methods to detect their output is of paramount importance. In this paper, we present GPT Reddit Dataset (GRiD), a novel Generative Pretrained Transformer (GPT)-generated text detection dataset designed to assess the performance of detection models in identifying generated responses from ChatGPT. The dataset consists of a diverse collection of context-prompt pairs based on Reddit, with human-generated and ChatGPT-generated responses. We provide an analysis of the dataset's characteristics, including linguistic diversity, context complexity, and response quality. To showcase the dataset's utility, we benchmark several detection methods on it, demonstrating their efficacy in distinguishing between human and ChatGPT-generated responses. This dataset serves as a resource for evaluating and advancing detection techniques in the context of ChatGPT and contributes to the ongoing efforts to ensure responsible and trustworthy AI-driven communication on the internet. Finally, we propose GpTen, a novel tensor-based GPT text detection method that is semi-supervised in nature since it only has access to human-generated text and performs on par with fully-supervised baselines.
Rumor mitigation in social media platforms with deep reinforcement learning
Authors: Hongyuan Su, Yu Zheng, Jingtao Ding, Depeng Jin, Yong Li
Venue: Conference WWW 2024
Year: 2024

Social media platforms have become one of the main channels where people disseminate and acquire information, of which the reliability is severely threatened by rumors widespread in the network. Existing approaches such as suspending users or broadcasting real information to combat rumors are either with high cost or disturbing users. In this paper, we introduce a novel rumor mitigation paradigm, where only a minimal set of links in the social network are intervened to decelerate the propagation of rumors, countering misinformation with low business cost and user awareness. A knowledge-informed agent embodying rumor propagation mechanisms is developed, which intervenes the social network with a graph neural network for capturing information flow in the social media platforms and a policy network for selecting links. Experiments on real social media platforms demonstrate that the proposed approach can effectively alleviate the influence of rumors, substantially reducing the affected populations by over 25%. Codes for this paper are released at https://github.com/tsinghua-fib-lab/DRL-Rumor-Mitigation.
Is this the era of misinformation yet: Combining social bots and fake news to deceive the masses
Authors: Patrick Wang, Rafael Angarita, Ilaria Renna
Venue: Conference WWW 2018
Year: 2018

Social media is an amazing platform for enhancing public exposure. Anyone, even social bots, can reach out to a vast community and expose one's opinion. But what happens when fake news is (un)intentionally spread within a social media This paper reviews techniques that can be used to fabricate fake news and depicts a scenario where social bots evolve in a fully semantic Web to infest social media with automatically generated deceptive information.
Fighting against fake news on newly-emerging crisis: A case study of covid-19
Authors: Migyeong Yang, Chaewon Park, Jiwon Kang, Daeun Lee, Daejin Choi, Jinyoung Han
Venue: Conference WWW 2024
Year: 2024

As social media users can easily access, generate, and spread information regardless of its authenticity, the proliferation of fake news related to public health has become a serious problem. Since these rumors have caused severe social issues, detecting them in the early stage is imminent. Therefore, in this paper, we propose a deep learning model that can debunk fake news on COVID-19, as a case study, at the initial stage of emergence. The evaluation with a newly-collected dataset consisting of both the COVID-19 and Non-COVID-19 fake news claims demonstrates that the proposed model achieves high performance, indicating that the model can identify fake news on COVID-19 in the early stage with a small amount of data. We believe that our methodology and findings can be applied to detect fake news on newly-emerging and critical topics, which should be performed with insufficient resources.
Optimal Morphological Approaches to Image Matching and Object Detection
Authors: Petros Maragos
Venue: Conference ICCV 1988
Year: 1988

It is shown that minimizing the C1 match- ing error is equivalent to maximizing a nonlinear signal cor- relation (a sum of minima), which is related to morpholog- ical filtering. This approach is optimum in rnany formula- tions of the image matching and object detection problem. Further, a closely related approach is outlined for object detection using rank order filters.
Sequential monte carlo fusion of sound and vision for speaker tracking
Authors: Jaco Vermaak, Michel Gangnet, Andrew Blake 0001, Patrick Pérez
Venue: Conference ICCV 2001
Year: 2001

Video telephony could be considerably enhanced by provision of a tracking system that allows freedom of movement to the speaker while maintaining a well-framed image, for transmission over limited bandwidth. Already commercial multi-microphone systems exist which track speaker direction in order to reject background noise. Stereo sound and vision are complementary modalities in that sound is good for initialisation (where vision is expensive) whereas vision is good for localisation (where sound is less precise). Using generative probabilistic models and particle filtering, we show that stereo sound and vision can indeed be fused effectively, to make a system more capable than with either modality on its own.
Boosting image retrieval
Authors: Kinh Tieu, Paul A. Viola
Venue: Conference CVPR 2000
Year: 2000

We present an approach for image retrieval using a very large number of highly selective features and efficient online learning. Our approach is predicated on the assumption that each image is generated by a sparse set of visual "causes" and that images which are visually similar share causes. We propose a mechanism for computing a very large number of highly selective features which capture some aspects of this causal structure (in our implementation there are over 45,000 highly selective features). At query time a user selects a few example images, and a technique known as "boosting" is used to learn a classification function in this feature space. By construction, the boosting procedure learns a simple classifier which only relies on 20 of the features. As a result a very large database of images can be scanned rapidly, perhaps a million images per second. Finally we will describe a set of experiments performed using our retrieval system on a database of 3000 images.