OpenRaiser/Intern-Atlas huggingface dataset:
"I'm always in so much pain and no one will understand" - Detecting Patterns in Suicidal Ideation on Reddit
Authors: Michal Monselise, Christopher C. Yang
Venue: WWW
Year: 2022

Social media has become another venue for those struggling with thoughts of suicide. Many turn to social media to express suicidal ideation and look for peer support. In our study we seek to better understand patterns in the behaviors of these users particularly on the social media platform Reddit. This study will explore how Reddit users move or progress between subreddits until they express active suicidal ideation. We also look at these users’ posting pattern in the time leading up to expressing suicidal ideation and the time after. We examined a large dataset of posts from users who created at least one thread on SuicideWatch during January 2019 - August 2019 and collected their posts starting in July 2018 to create a look back period of 6 months. This generated a total of 5,892,310 posts. We defined what it means to progress between subreddits and generated a graph of progressions of all users in our dataset. We found that these users mostly progressed to or from 8 different subreddits and each of these subreddits could point to a particular emotional difficulty that a user was having such as self harm or relationship problems. Furthermore, we examined the volume of posts and the proportion of posts with negative sentiment leading up to the first incident of active suicidal ideation and found that there is an increase in both negative sentiment and volume of posts leading up to the day of the first incident of suicidal ideation on Reddit. However, during the day of first incident of suicidal ideation, there is a precipitous drop in the number of posts which goes back up on the following day. Using this insight, we can better understand these users. This will allow for developing intervention for suicide prevention in social media platforms in the future.
Effective Messaging on Social Media: What Makes Online Content Go Viral?
Authors: Maryam Mousavi, Hasan Davulcu, Mohsen Ahmadi, Robert Axelrod, Richard Davis, Scott Atran
Venue: WWW
Year: 2022

In this paper, we propose and test three content-based hypotheses that significantly increase message virality. We measure virality as the retweet counts of messages in a pair of real-world Twitter datasets A large dataset - UK Brexit with 51 million tweets from 2.8 million users between June 1, 2015 and May 12, 2019 and a smaller dataset - Nord Stream 2 with 516,000 tweets from 250,000 users between October 1, 2019 and October 15, 2019. We hypothesize, test and conclude that messages incorporating “negativity bias”, “causal arguments” and “threats to personal or societal core values of target audiences” singularly and jointly increase message virality on social media.
Citizens as Developers and Consumers of Smart City Services: A Drone Tour Guide Case
Authors: Christian Muck, Alexander Völz, Danial Mohammadi Amlashi, Dimitris Karagiannis
Venue: WWW
Year: 2022

The trend of urbanization has started over two centuries ago and is no longer limited to high-income countries. As a result, city population growth has led to the emergence of applications that manage complex processes within cities by utilizing recent technological advances, thereby transforming them into smart cities. Besides automating complex processes within a city, technology also enables a simplified integration of citizens into identifying problems and creating corresponding solutions. This paper discusses an approach that enables citizens to design and later execute their own services within a smart city environment by employing conceptual modeling and microservices. The overall aim is to establish the role of a citizen developer. The proposed approach is then discussed within our proof of concept environment based on a drone tour guide case.
DCAF-BERT: A Distilled Cachable Adaptable Factorized Model For Improved Ads CTR Prediction
Authors: Aashiq Muhamed, Jaspreet Singh, Shuai Zheng, Iman Keivanloo, Sujan Perera, James Mracek, Yi Xu, Qingjun Cui, Santosh Rajagopalan, Belinda Zeng, Trishul Chilimbi
Venue: WWW
Year: 2022

In this paper we present a Click-through-rate (CTR) prediction model for product advertisement at Amazon. CTR prediction is challenging because the model needs to a) learn from text and numeric features, b) maintain low-latency at inference time, and c) adapt to a temporal advertisement distribution shift. Our proposed model is DCAF-BERT, a novel lightweight cache-friendly factorized model that consists of twin-structured BERT-like encoders for text with a mechanism for late fusion for tabular and numeric features. The factorization of the model allows for compartmentalised retraining which enables the model to easily adapt to distribution shifts. The twin encoders are carefully trained to leverage historical CTR data, using a large pre-trained language model and cross-architecture knowledge distillation (KD). We empirically find the right combination of pretraining, distillation and fine-tuning strategies for teacher and student which leads to a 1.7% ROC-AUC lift over the previous best model offline. In an online experiment we show that our compartmentalised refresh strategy boosts the CTR of DCAF-BERT by 3.6% on average over the baseline model consistently across a month.
Identification of Disease or Symptom terms in Reddit to Improve Health Mention Classification
Authors: Usman Naseem, Jinman Kim, Matloob Khushi, Adam G. Dunn
Venue: WWW
Year: 2022

In a user-generated text such as on social media platforms and online forums, people often use disease or symptom terms in ways other than to describe their health. In data-driven public health surveillance, the health mention classification (HMC) task aims to identify posts where users are discussing health conditions rather than using disease and symptom terms for other reasons. Existing computational research typically only studies health mentions in Twitter, with limited coverage of disease or symptom terms, ignore user behavior information, and other ways people use disease or symptom terms. To advance the HMC research, we present a Reddit health mention dataset (RHMD), a new dataset of multi-domain Reddit data for the HMC. RHMD consists of 10,015 manually labeled Reddit posts that mention 15 common disease or symptom terms and are annotated with four labels: namely personal health mentions, non-personal health mentions, figurative health mentions, and hyperbolic health mentions. With RHMD, we propose HMCNET that combines a target keyword (disease or symptom term) identification and user behavior hierarchically to improve HMC. Experimental results demonstrate that the proposed approach outperforms state-of-the-art methods with an F1-Score of 0.75 (an increase of 11% over the state-of-the-art) and shows that our new dataset poses a strong challenge to the existing HMC methods.
A Generative Approach for Financial Causality Extraction
Authors: Tapas Nayak, Soumya Sharma, Yash Butala, Koustuv Dasgupta, Pawan Goyal, Niloy Ganguly
Venue: WWW
Year: 2022

Causality represents the foremost relation between events in financial documents such as financial news articles, financial reports. Each financial causality contains a cause span and an effect span. Previous works proposed sequence labeling approaches to solve this task. But sequence labeling models find it difficult to extract multiple causalities and overlapping causalities from the text segments. In this paper, we explore a generative approach for causality extraction using the encoder-decoder framework and pointer networks. We use a causality dataset from the financial domain, FinCausal, for our experiments and our proposed framework achieves very competitive performance on this dataset.
Choice of Implicit Signal Matters: Accounting for User Aspirations in Podcast Recommendations
Authors: Zahra Nazari, Praveen Chandar, Ghazal Fazelnia, Catherine M. Edwards, Benjamin A. Carterette, Mounia Lalmas
Venue: WWW
Year: 2022

Recommender systems are modulating what billions of people are exposed to on a daily basis. Typically, these systems are optimized for user engagement signals such as clicks, streams, likes, or a weighted combination of such sets. Despite the pervasiveness of this practice, little research has been done to explore the downstream impacts of optimization choice on users, creators and the ecosystem they are offered in. We used a platform that caters recommendations to millions of people and show in practice what you optimize for can have a large impact on the content users are exposed to, as well as what they end up consuming. In this work, we use podcast recommendations with two engagement signals: Subscription vs. Plays to show that the choice of user engagement matters. We deployed recommendation models optimized for each signal in production and observed that consumption outcomes substantially defer depending on the target used. Upon further investigation, we observed that users’ patterns of podcast engagement depend on the type of podcast, and each podcast can cater to specific user goals & needs. Optimizing for streams can bias the recommendations towards certain podcast types, undermine users’ aspirational interests and put some show categories at disadvantage. Finally, using calibration we demonstrate that informed balanced recommendations can help address this issue and thereby satisfy diverse user interests.
Rows from Many Sources: Enriching row completions from Wikidata with a pre-trained Language Model
Authors: Carina Negreanu, Alperen Karaoglu, Jack Williams, Shuang Chen, Daniel Fabian, Andrew D. Gordon, Chin-Yew Lin
Venue: WWW
Year: 2022

Row completion is the task of augmenting a given table of text and numbers with additional, relevant rows. The task divides into two steps: subject suggestion, the task of populating the main column; and gap filling, the task of populating the remaining columns. We present state-of-the-art results for subject suggestion and gap filling measured on a standard benchmark (WikiTables). Our idea is to solve this task by harmoniously combining knowledge base table interpretation and free text generation. We interpret the table using the knowledge base to suggest new rows and generate metadata like headers through property linking. To improve candidate diversity, we synthesize additional rows using free text generation via GPT-3, and crucially, we exploit the metadata we interpret to produce better prompts for text generation. Finally, we verify that the additional synthesized content can be linked to the knowledge base or a trusted web source such as Wikipedia.
Universal Graph Transformer Self-Attention Networks
Authors: Dai Quoc Nguyen, Tu Dinh Nguyen, Dinh Q. Phung
Venue: WWW
Year: 2022

We introduce a transformer-based GNN model, named UGformer, to learn graph representations. In particular, we present two UGformer variants, wherein the first variant (publicized in September 2019) is to leverage the transformer on a set of sampled neighbors for each input node, while the second (publicized in May 2021) is to leverage the transformer on all input nodes. Experimental results demonstrate that the first UGformer variant achieves state-of-the-art accuracies on benchmark datasets for graph classification in both inductive setting and unsupervised transductive setting; and the second UGformer variant obtains state-of-the-art accuracies for inductive text classification. The code is available at: https://github.com/daiquocnguyen/Graph-Transformer.
Towards an Interpretable Approach to Classify and Summarize Crisis Events from Microblogs
Authors: Thi Huyen Nguyen, Koustav Rudra
Venue: WWW
Year: 2022

Microblogging platforms like Twitter have been heavily leveraged to report and exchange information about natural disasters. The real-time data on these sites is highly helpful in gaining situational awareness and planning aid efforts. However, disaster-related messages are immersed in a high volume of irrelevant information. The situational data of disaster events also vary greatly in terms of information types ranging from general situational awareness (caution, infrastructure damage, casualties) to individual needs or not related to the crisis. It thus requires efficient methods to handle data overload and prioritize various types of information. This paper proposes an interpretable classification-summarization framework that first classifies tweets into different disaster-related categories and then summarizes those tweets. Unlike existing work, our classification model can provide explanations or rationales for its decisions. In the summarization phase, we employ an Integer Linear Programming (ILP) based optimization technique along with the help of rationales to generate summaries of event categories. Extensive evaluation on large-scale disaster events shows (a). our model can classify tweets into disaster-related categories with an 85% Macro F1 score and high interpretability (b). the summarizer achieves (5-25%) improvement in terms of ROUGE-1 F-score over most state-of-the-art approaches.
Context-Enriched Learning Models for Aligning Biomedical Vocabularies at Scale in the UMLS Metathesaurus
Authors: Vinh Nguyen, Hong Yung Yip, Goonmeet Bajaj, Thilini Wijesiriwardene, Vishesh Javangula, Srinivasan Parthasarathy, Amit P. Sheth, Olivier Bodenreider
Venue: WWW
Year: 2022

The Unified Medical Language System (UMLS) Metathesaurus construction process mainly relies on lexical algorithms and manual expert curation for integrating over 200 biomedical vocabularies. A lexical-based learning model (LexLM) was developed to predict synonymy among Metathesaurus terms and largely outperforms a rule-based approach (RBA) that approximates the current construction process. However, the LexLM has the potential for being improved further because it only uses lexical information from the source vocabularies, while the RBA also takes advantage of contextual information. We investigate the role of multiple types of contextual information available to the UMLS editors, namely source synonymy (SS), source semantic group (SG), and source hierarchical relations (HR), for the UMLS vocabulary alignment (UVA) problem. In this paper, we develop multiple variants of context-enriched learning models (ConLMs) by adding to the LexLM the types of contextual information listed above. We represent these context types in context-enriched knowledge graphs (ConKGs) with four variants ConSS, ConSG, ConHR, and ConAll. We train these ConKG embeddings using seven KG embedding techniques. We create the ConLMs by concatenating the ConKG embedding vectors with the word embedding vectors from the LexLM. We evaluate the performance of the ConLMs using the UVA generalization test datasets with hundreds of millions of pairs. Our extensive experiments show a significant performance improvement from the ConLMs over the LexLM, namely +5.0% in precision (93.75%), +0.69% in recall (93.23%), +2.88% in F1 (93.49%) for the best ConLM. Our experiments also show that the ConAll variant including the three context types takes more time, but does not always perform better than other variants with a single context type. Finally, our experiments show that the pairs of terms with high lexical similarity benefit most from adding contextual information, namely +6.56% in precision (94.97%), +2.13% in recall (93.23%), +4.35% in F1 (94.09%) for the best ConLM. The pairs with lower degrees of lexical similarity also show performance improvement with +0.85% in F1 (96%) for low similarity and +1.31% in F1 (96.34%) for no similarity. These results demonstrate the importance of using contextual information in the UVA problem.
Enhancing Query Answer Completeness with Query Expansion based on Synonym Predicates
Authors: Emetis Niazmand
Venue: WWW
Year: 2022

Community-based knowledge graphs are generated following hybrid approaches, where human intelligence empowers computational methods to effectively integrate encyclopedic knowledge or provide a common understanding of a domain. Existing community-based knowledge graphs represent essential sources of knowledge for enhancing the accuracy of data mining, information retrieval, question answering, and multimodal processing. However, despite the enormous effort conducted by contributing communities, community-based knowledge graphs may be incomplete and integrate duplicated data and metadata. We tackle the problem of enhancing query answering against incomplete community-based knowledge graphs by proposing an efficient query processing approach to estimate answer completeness and increase the results. It assumes that community-based knowledge graphs comprise synonym predicates that complement knowledge graph triples required to raise query answering completeness. The aim is proposing a novel query expansion method based on synonym predicates identified using embeddings built on a knowledge graph. Our preliminary analysis shows that our approach improves query answer completeness. However, queries can be expanded with some similar predicates which do not lead to complete answers. This shows that more work is required for query expansion with the minimum synonym predicates that maximize answer completeness.
Multi-Tenancy in Smart City Platforms
Authors: Ioannis Nikolaou, Leonidas G. Anthopoulos
Venue: WWW
Year: 2022

Multi-tenancy emerged as a software architecture pattern in an effort to optimize the use of compute resources and minimize the operational cost of large scale deployments. Its applicability, however, needs to take into account each particular context as the challenges of this software architecture pattern may not make it an optimal choice in every situation. A Smart City Platform is by definition a type of software that is also expected to be deployed at a large scale. The applicability of the multi-tenancy architecture pattern in this context is debatable as the benefits it brings may not outweigh the challenges.
What Should You Know? A Human-In-the-Loop Approach to Unknown Unknowns Characterization in Image Recognition
Authors: Shahin Sharifi Noorian, Sihang Qiu, Ujwal Gadiraju, Jie Yang, Alessandro Bozzon
Venue: WWW
Year: 2022

Unknown unknowns represent a major challenge in reliable image recognition. Existing methods mainly focus on unknown unknowns identification, leveraging human intelligence to gather images that are potentially difficult for the machine. To drive a deeper understanding of unknown unknowns and more effective identification and treatment, this paper focuses on unknown unknowns characterization. We introduce a human-in-the-loop, semantic analysis framework for characterizing unknown unknowns at scale. We engage humans in two tasks that specify what a machine should know and describe what it really knows, respectively, both at the conceptual level, supported by information extraction and machine learning interpretability methods. Data partitioning and sampling techniques are employed to scale out human contributions in handling large data. Through extensive experimentation on scene recognition tasks, we show that our approach provides a rich, descriptive characterization of unknown unknowns and allows for more effective and cost-efficient detection than the state of the art.
VisGNN: Personalized Visualization Recommendationvia Graph Neural Networks
Authors: Fayokemi Ojo, Ryan A. Rossi, Jane Hoffswell, Shunan Guo, Fan Du, Sungchul Kim, Chang Xiao, Eunyee Koh
Venue: WWW
Year: 2022

In this work, we develop a Graph Neural Network (GNN) framework for the problem of personalized visualization recommendation. The GNN-based framework first represents the large corpus of datasets and visualizations from users as a large heterogeneous graph. Then, it decomposes a visualization into its data and visual components, and then jointly models each of them as a large graph to obtain embeddings of the users, attributes (across all datasets in the corpus), and visual-configurations. From these user-specific embeddings of the attributes and visual-configurations, we can predict the probability of any visualization arising from a specific user. Finally, the experiments demonstrated the effectiveness of using graph neural networks for automatic and personalized recommendation of visualizations to specific users based on their data and visual (design choice) preferences. To the best of our knowledge, this is the first such work to develop and leverage GNNs for this problem.
Making Adversarially-Trained Language Models Forget with Model Retraining: A Case Study on Hate Speech Detection
Authors: Marwan Omar, David Mohaisen
Venue: WWW
Year: 2022

Adversarial training has become almost the de facto standard for robustifying Natural Language Processing models against adversarial attacks. Although adversarial training has proven to achieve accuracy gains and boost the performance of algorithms, research has not shown how adversarial training will stand “the test of times” when models are deployed and updated with new non-adversarial data samples. In this study, we aim to quantify the temporal impact of adversarial training on naturally-evolving language models using the hate speech task. We conduct extensive experiments on the Tweet Eval benchmark dataset using multiple hate speech classification models. In particular, our findings indicate that adversarial training is highly task-dependent as well as dataset dependent as models trained on the same dataset achieve high prediction accuracy but fare poorly when tested with new dataset even after retraining models with adversarial examples. We attribute this temporal and limited effect of adversarial training to distribution shift of the training data which implies that models’ quality will degrade over-time as models are deployed in the real world and start serving new data.
CCGG: A Deep Autoregressive Model for Class-Conditional Graph Generation
Authors: Yassaman Ommi, Matin Yousefabadi, Faezeh Faez, Amirmojtaba Sabour, Mahdieh Soleymani Baghshah, Hamid R. Rabiee
Venue: WWW
Year: 2022

Graph data structures are fundamental for studying connected entities. With an increase in the number of applications where data is represented as graphs, the problem of graph generation has recently become a hot topic. However, despite its significance, conditional graph generation that creates graphs with desired features is relatively less explored in previous studies. This paper addresses the problem of class-conditional graph generation that uses class labels as generation constraints by introducing the Class Conditioned Graph Generator (CCGG). We built CCGG by injecting the class information as an additional input into a graph generator model and including a classification loss in its total loss along with a gradient passing trick. Our experiments show that CCGG outperforms existing conditional graph generation methods on various datasets. It also manages to maintain the quality of the generated graphs in terms of distribution-based evaluation metrics.
Stylistic Control for Neural Natural Language Generation
Authors: Shereen Oraby
Venue: WWW
Year: 2022

With the rise of conversational assistants, it has become more critical for dialog systems to keep users engaged by responding in a natural, interesting, and often personalized way, even in a task-oriented setting. Recent work has thus focused on stylistic control for natural language generation (NLG) systems in order to jointly control response semantics and style. In this talk, I will describe our work on automatic data curation and modeling approaches to facilitate style control for both personality-specific attributes of style (based on Big-Five personality traits), and other style attributes that are helpful for personalization, e.g., response length, descriptiveness, point-of-view, and sentiment. I will present work that incorporates these attributes into the training and generation pipelines for different NLG architectures, and will show how our data curation and modeling approaches are generalizable to new domains and style choices. Finally, I will describe how we use a combination of automatic and human evaluation methods to measure how well models successfully hit multiple style targets without sacrificing semantics.