OpenRaiser/Intern-Atlas huggingface dataset:
Et tu, Brute? Privacy Analysis of Government Websites and Mobile Apps
Authors: Nayanamana Samarasinghe, Aashish Adhikari, Mohammad Mannan, Amr M. Youssef
Venue: WWW
Year: 2022

Past privacy measurement studies on web tracking focused on high-ranked commercial websites, as user tracking is extensively used for monetization on those sites. Conversely, governments across the globe now offer services online, which unlike commercial sites, are funded by public money, and do not generally make it to the top million website lists. As such, web tracking on those services has not been comprehensively studied, even though these services deal with privacy and security-sensitive user data, and used by a significant number of users. In this paper, we perform privacy and security measurements on government websites and Android apps: 150,244 unique websites (from 206 countries) and 1166 Android apps (from 71 countries). We found numerous commercial trackers on these services—e.g., 17% of government websites and 37% of government Android apps host Google trackers; 13% of government sites contain YouTube cookies with an expiry date in the year of 9999. 27% of government Android apps leak sensitive information (e.g., user/device identifiers, passwords, API keys) to third parties, or any network attacker (when sent over HTTP). We also found 304 government sites and 40 apps are flagged by VirusTotal as malicious. We hope our findings to help improve privacy and security of online government services, given that governments are now apparently taking Internet privacy/security seriously and imposing strict regulations on commercial sites.
Scriptoria: A Crowd-powered Music Transcription System
Authors: Ioannis Petros Samiotis, Christoph Lofi, Shaad Alaka, Cynthia C. S. Liem, Alessandro Bozzon
Venue: WWW
Year: 2022

In this demo we present Scriptoria, an online crowdsourcing system to tackle the complex transcription process of classical orchestral scores. The system’s requirements are based on experts’ feedback from classical orchestra members. The architecture enables an end-to-end transcription process (from PDF to MEI) using a scalable microtask design. Reliability, stability, task and UI design were also evaluated and improved through Focus Group Discussions. Finally, we gathered valuable comments on the transcription process itself alongside future additions that could greatly enhance current practices in their field.
SchemaWalk: Schema Aware Random Walks for Heterogeneous Graph Embedding
Authors: Ahmed E. Samy, Lodovico Giaretta, Zekarias T. Kefato, Sarunas Girdzijauskas
Venue: WWW
Year: 2022

Heterogeneous Information Network (HIN) embedding has been a prevalent approach to learn representations off semantically-rich heterogeneous networks. Most HIN embedding methods exploit meta-paths to retain high-order structures, yet, their performance is conditioned on the quality of the (generated/manually-defined) meta-paths and their suitability for the specific label set. Whereas other methods adjust random walks to harness or skip certain heterogeneous structures (e.g. node type(s)), in doing so, the adjusted random walker may casually omit other node/edge types. Our key insight is with no domain knowledge, the random walker should hold no assumptions about heterogeneous structure (i.e. edge types). Thus, aiming for a flexible and general method, we utilize network schema as a unique blueprint of HIN, and propose SchemaWalk, a random walk to uniformly sample all edge types within the network schema. Moreover, we identify the starvation phenomenon which induces random walkers on HINs to under- or over-sample certain edge types. Accordingly, we design SchemaWalkHO to skip local deficient connectivity to preserve uniform sampling distribution. Finally, we carry out node classification experiments on four real-world HINs, and provide in-depth qualitative analysis. The results highlight the robustness of our method regardless to the graph structure in contrast with the state-of-the-art baselines.
Streaming Analytics with Adaptive Near-data Processing
Authors: Atul Sandur, Chanho Park, Stavros Volos, Gul Agha, Myeongjae Jeon
Venue: WWW
Year: 2022

Streaming analytics applications need to process massive volumes of data in a timely manner, in domains ranging from datacenter telemetry and geo-distributed log analytics to Internet-of-Things systems. Such applications suffer from significant network transfer costs to transport the data to a stream processor and compute costs to analyze the data in a timely manner. Pushing the computation closer to the data source by partitioning the analytics query is an effective strategy to reduce resource costs for the stream processor. However, the partitioning strategy depends on the nature of resource bottleneck and resource variability that is encountered at the compute resources near the data source. In this paper, we investigate different issues which affect query partitioning strategies. We first study new partitioning techniques within cloud datacenters which operate under constrained compute conditions varying widely across data sources and different time slots. With insights obtained from the study, we suggest several different ways to improve the performance of stream analytics applications operating in different resource environments, by making effective partitioning decisions for a variety of use cases such as geo-distributed streaming analytics.
Concept Annotation from Users Perspective: A New Challenge
Authors: Souvika Sarkar, Shubhra Kanti Karmaker Santu
Venue: WWW
Year: 2022

Text data is highly unstructured and can often be viewed as a complex representation of different concepts, entities, events, sentiments etc. For a wide variety of computational tasks, it is thus very important to annotate text data with the associated concepts / entities, which can put some initial structure / index on raw text data. However, It is not feasible to manually annotate a large amount of text, raising the need for automatic text annotation. In this paper, we focus on concept annotation in text data from the perspective of real world users. Concept annotation is not a trivial task and its utility often highly relies on the preference of the user. Despite significant progress in natural language processing research, we still lack a general purpose concept annotation tool which can effectively serve users from a wide range of application domains. Thus, further investigation is needed from a user-centric point of view to design an automated concept annotation tool that will ensure maximum utility to its users. To achieve this goal, we created a benchmark corpus of two real world data-sets, i.e., “News Concept Data-set” and “Medical Concept Data-set”, to introduce the notion of user-oriented concept annotation and provide a way to evaluate this task. The term “user-centric” means that the desired concepts are defined as well as characterized by the users themselves. Throughout the paper, we describe the details about how we created the data-sets, what are the unique characteristics of each data-set, how these data-sets reflect real users perspective for the concept annotation task, and finally, how they can serve as a great resource for future research on user-centric concept annotation.
Influence of Language Proficiency on the Readability of Review Text and Transformer-based Models for Determining Language Proficiency
Authors: Salim Sazzed
Venue: WWW
Year: 2022

In this study, we analyze the influence of the English language proficiency of non-native speakers on the readability of the text written by them. In addition, we present multiple approaches for automatically determining the language proficiency levels of non-native English speakers from the review data. To accomplish the above-mentioned tasks, we first introduce an annotated social media corpus of around 1000 reviews written by non-native English speakers of the following five English language proficiency (ELP) groups: very high proficiency (VHP), high proficiency (HP), moderate proficiency (MP), low proficiency (LP), and very low proficiency (VLP). We employ the Flesch Reading Ease (FLE) and Flesch-Kincaid Grade (FKG) tests to compute the readability scores of the reviews written by various ELP groups. We leverage both the classical machine learning (ML) classifiers and transformer-based language models for deciding the language proficiency groups of the reviewers. We observe that distinct ELP groups do not exhibit any noticeable differences in the mean FRE scores, although slight differences are observed in the FKG test. The results imply that the readability measures do not possess high discriminating capabilities to distinguish various ELP groups. In the language proficiency determination task, we notice fine-tuned transformer-based approaches yield slightly better efficacy than the traditional ML classifiers.
ECCE: Entity-centric Corpus Exploration Using Contextual Implicit Networks
Authors: Julian Schelb, Maud Ehrmann, Matteo Romanello, Andreas Spitz
Venue: WWW
Year: 2022

In the Digital Age, the analysis and exploration of unstructured document collections is of central importance to members of investigative professions, whether they might be scholars, journalists, paralegals, or analysts. In many of their domains, entities play a key role in the discovery of implicit relations between the contents of documents and thus serve as natural entry points to a detailed manual analysis, such as the prototypical 5Ws in journalism or stock symbols in finance. To assist in these analyses, entity-centric networks have been proposed as a language model that represents document collections as a cooccurrence graph of entities and terms, and thereby enables the visual exploration of corpora. Here, we present ECCE, a web-based application that implements entity-centric networks, augments them with contextual language models, and provides users with the ability to upload, manage, and explore document collections. Our application is available as a web-based service at http://dimtools.uni.kn/ecce.
Are democratic User Groups More Inclusive?
Authors: Anass Sedrati, Reda Benkhadra
Venue: WWW
Year: 2022

User groups form an important part of the Wikimedia movement and ecosystem. They gather a big number of free knowledge enthusiasts both online and offline, having either a shared geographical location or thematic interest, or both. At their creation, these structures and their volunteer founders did not receive any formal introduction nor guidelines on ways of working. Governance of these groups lies then between the hands of their founders, who can have different proceeding methods and approaches, depending on their background and vision. In the past, several conflicts related to user group management have occurred, where words such as “transparency” and “democracy” were mentioned as solutions to the current gaps and lack of inclusivity in the groups. The current paper intends to investigate the veracity of this claim, through detailing and understanding the concept of democracy, most specifically in digital online communities, such as Wikimedia volunteer user groups. Since the question remains open, the paper's addition is the elements provided to enrich the discussion, highlighting the concerns raised in different communities.
How Do Mothers and Fathers Talk About Parenting to Different Audiences?: Stereotypes and Audience Effects: An Analysis of r/Daddit, r/Mommit, and r/Parenting Using Topic Modelling
Authors: Melody Sepahpour-Fard, Michael Quayle
Venue: WWW
Year: 2022

While major strides have been made towards gender equality in public life, serious inequality remains in the domestic sphere, especially around parenting. The present study analyses discussions about parenting on Reddit (i.e., a content aggregation website) to explore audience effects and gender stereotypes. It suggests a novel method to study topical variation in individuals’ language when interacting with different audiences. Comments posted in 2020 were collected from three parenting subreddits (i.e., topical communities), described as being for fathers (r/Daddit), mothers (r/Mommit), and all parents (r/Parenting). Users posting on r/Parenting and r/Daddit or on r/Parenting and r/Mommit were assumed to identify as fathers or mothers, respectively, allowing gender comparison. Users’ comments on r/Parenting (to a mixed-gender audience) were compared with their comments to single-gender audiences on r/Daddit or r/Mommit using Latent Dirichlet Allocation (LDA) topic modelling. Results show that the most discussed topic among parents is about education and family advice, a topic mainly discussed in the mixed-gender subreddit and more by fathers than mothers. The topic model also indicates that, when it comes to the basic needs of children (sleep, food, and medical care), mothers seem to be more concerned regardless of the audience. In contrast, topics such as birth and pregnancy announcements and physical appearance are more discussed by fathers in the father-centric subreddit. Overall, findings seem to show that mothers are generally more concerned about the practical sides of parenting while fathers’ expressed concerns are more contextual: with other fathers, there seems to be a desire to show their fatherhood and be recognized for it while they discuss education with mothers. These results demonstrate that concerns expressed by parents on Reddit are context-sensitive but also consistent with gender stereotypes, potentially reflecting a persistent gendered and unequal division of labour in parenting.
Construction of Large-Scale Misinformation Labeled Datasets from Social Media Discourse using Label Refinement
Authors: Karishma Sharma, Emilio Ferrara, Yan Liu
Venue: WWW
Year: 2022

Malicious accounts spreading misinformation has led to widespread false and misleading narratives in recent times, especially during the COVID-19 pandemic, and social media platforms struggle to eliminate these contents rapidly. This is because adapting to new domains requires human intensive fact-checking that is slow and difficult to scale. To address this challenge, we propose to leverage news-source credibility labels as weak labels for social media posts and propose model-guided refinement of labels to construct large-scale, diverse misinformation labeled datasets in new domains. The weak labels can be inaccurate at the article or social media post level where the stance of the user does not align with the news source or article credibility. We propose a framework to use a detection model self-trained on the initial weak labels with uncertainty sampling based on entropy in predictions of the model to identify potentially inaccurate labels and correct for them using self-supervision or relabeling. The framework will incorporate social context of the post in terms of the community of its associated user for surfacing inaccurate labels towards building a large-scale dataset with minimum human effort. To provide labeled datasets with distinction of misleading narratives where information might be missing significant context or has inaccurate ancillary details, the proposed framework will use the few labeled samples as class prototypes to separate high confidence samples into false, unproven, mixture, mostly false, mostly true, true, and debunk information. The approach is demonstrated for providing a large-scale misinformation dataset on COVID-19 vaccines.
FinRED: A Dataset for Relation Extraction in Financial Domain
Authors: Soumya Sharma, Tapas Nayak, Arusarka Bose, Ajay Kumar Meena, Koustuv Dasgupta, Niloy Ganguly, Pawan Goyal
Venue: WWW
Year: 2022

This paper presents the results of research on supervised extractive text summarisation for scientific articles. We show that a simple sequential tagging model based only on the text within a document achieves high results against a simple classification model. Improvements can be achieved through additional sentence-level features, though these were minimal. Through further analysis, we show the potential of the sequential model relying on the structure of the document depending on the academic discipline which the document is from.
Learning-based Fuzzy Bitrate Matching at the Edge for Adaptive Video Streaming
Authors: Wanxin Shi, Qing Li, Chao Wang, Longhao Zou, Gengbiao Shen, Pei Zhang, Yong Jiang
Venue: WWW
Year: 2022

The rapid growth of video traffic imposes significant challenges on content delivery over the Internet. Meanwhile, edge computing is developed to accelerate video transmission as well as release the traffic load of origin servers. Although some related techniques (e.g., transcoding and prefetching) are proposed to improve edge services, they cannot fully utilize cached videos. Therefore, we propose a Learning-based Fuzzy Bitrate Matching scheme (LFBM) at the edge for adaptive video streaming, which utilizes the capacity of network and edge servers. In accordance with user requests, cache states and network conditions, LFBM utilizes reinforcement learning to make a decision, either fetching the video of the exact bitrate from the origin server or responding with a different representation from the edge server. In the simulation, compared with the baseline, LFBM improves cache hit ratio by 128%. Besides, compared with the scheme without fuzzy bitrate matching, it improves Quality of Experience (QoE) by 45%. Moreover, the real-network experiments further demonstrate the effectiveness of LFBM. It increases the hit ratio by 84% compared with the baseline and improves the QoE by 51% compared with the scheme without fuzzy bitrate matching.
Understanding Dropout for Graph Neural Networks
Authors: Juan Shu, Bowei Xi, Yu Li, Fan Wu, Charles A. Kamhoua, Jianzhu Ma
Venue: WWW
Year: 2022

Graph neural network (GNN) has demonstrated superior performance on graph learning tasks. GNN captures the data dependencies via message passing amid neural networks. Hence the prediction of a node label can utilize information from its neighbors in a graph. Dropout is a regularization as well as an ensemble method for convolutional neural network (CNN), which has been carefully studied. However, there are few existing works that focused on dropout schemes for GNN. Although GNN and CNN share similar model architecture, both with convolutional layers and fully connected layers, the input data structure for GNN and CNN are different and convolution operation differs. This suggests the dropout schemes for CNN should not be directly applied to GNN without a good understanding of the impact. In this paper, we divide the existing dropout schemes for GNN into two categories: (1) dropout on feature maps and (2) dropout on graph structure. Based on the drawbacks of current GNN dropout models, we propose a novel layer compensation dropout and a novel adaptive heteroscadestic Gaussian dropout, which can be applied to any type of GNN models and outperforms their corresponding baselines in shallow GNNs. Then an experimental study shows Bernoulli dropout generalize better while Gaussian dropout is slightly stronger in transductive performance. At last, we theoretically study how different dropout schemes mitigate over-smoothing problems and experimental results shows that layer compensation dropout allows a GNN model to maintain or slightly improve its performance as the GNN model adds more layers while all the other dropout models suffer from performance degradation when GNN goes deep.
A Bi-level Assessment of Twitter Data for Election Prediction: Delhi Assembly Elections 2020
Authors: Maneet Singh, S. R. S. Iyengar, Akrati Saxena, Rishemjit Kaur
Venue: WWW
Year: 2022

Elections are the backbone of any democratic country, where voters elect the candidates as their representatives. The emergence of social networking sites has provided a platform for political parties and their candidates to connect with voters in order to spread their political ideas. Our study aims to use Twitter in assessing the outcome of the Delhi Assembly elections held in 2020, using a bi-level approach, i.e., concerning political parties and their candidates. We analyze the correlation of election results with the activities of different candidates and parties on Twitter, and the response of voters on them, especially the mentions and sentiment of voters towards a party over time. The Twitter profiles of the candidates are compared both at the party level as well as the candidate level to evaluate their association with the outcome of the election. We observe that the number of followers and the replies to candidates’ tweets are good indicators for predicting actual election outcomes. However, we observe that the number of tweets mentioning a party and the temporal analysis of voters’ sentiment towards the party shown in tweets are not aligned with the election result. Moreover, the variations in the activeness of candidates and political parties on Twitter with time also not very helpful in identifying the winner. Thus, merely using temporal data from Twitter is not sufficient to make accurate predictions, especially for countries like India.
Mining Homophilic Groups of Users using Edge Attributed Node Embedding from Enterprise Social Networks
Authors: Priyanka Sinha, Ritu Patel, Pabitra Mitra, Dilys Thomas, Lipika Dey
Venue: WWW
Year: 2022

We develop a method to identify groups of similarly behaving users with similar work contexts from their activity on enterprise social media. This would allow organizations to discover redundancies and increase efficiency. To better capture the network structure and communication characteristics, we model user communications with directed attributed edges in a graph. Communication parameters including engagement frequency, emotion words, and post lengths act as edge weights of the multiedge. Upon the resultant adjacency tensor, we develop a node embedding algorithm using higher order singular value tensor decomposition and convolutional autoencoder. We develop a peer group identification algorithm using the cluster labels obtained from the node embedding and show its results on Enron emails and StackExchange Workplace community. We observe that people of the same roles in enterprise social media are clustered together by our method. We provide a comparison with existing node embedding algorithms as a reference indicating that attributed social networks and our formulations are an efficient and scalable way to identify peer groups in an enterprise social network that aids in professional social matching.
A Two-stage User Intent Detection Model on Complicated Utterances with Multi-task Learning
Authors: Shuangyong Song, Xiangyan Chen, Chao Wang, Xiaoguang Yu, Jia Wang, Xiaodong He
Venue: WWW
Year: 2022

As one of the most natural manner of human–machine interaction, the dialogue systems have attracted much attention in recent years, such as chatbots and intelligent customer service bots, etc. Intents of concise user utterances can be easily detected with classic text classification models or text matching models, while complicated utterances are harder to understand directly. In this paper, for improving the user intent detection from complicated utterances in an intelligent customer service bot JIMI (JD Instant Messaging intelligence), which is designed for creating an innovative online shopping experience in E-commerce, we propose a two-stage model which combines sentence Compression and intent Classification together with Multi-task learning, called MCC. Besides, a dialogue-oriented language model is trained to further improve the performance of MCC. Experimental results show that our model can achieve good performance on both a public dataset and the JIMI dataset.