Reviewerly/exHarmony huggingface dataset:
ROBUREC: Building a Robust Recommender using Autoencoders with Anomaly Detection

In the realm of social network analysis and mining, recommendation systems have become indispensable algorithms in assisting users and industries in navigating the available contents or products in various domains and getting the most personalized recommendations to their interests and preferences. However, if the input data has been generated by malicious users, that poses a significant challenge to recommender systems' reliability and efficiency. One of the main threats that poses a challenge to recommender systems is shilling attacks. Shilling attacks tend to manipulate or poison the data in the systems' training phase, leading to biased or compromised recommendations. To address this challenge, we propose a robust recommender system using variational autoencoders (VAE) with Anomaly detection. Our model learns complex and non-linear patterns by exclusively focusing on the user-item interaction data, represented by a binary user-item interaction matrix, making it more resilient to classic shilling attacks. Moreover, our paper incorporates an anomaly detection mechanism, alongside the autoencoder, that analyzes the reconstruction errors, i.e. (MSE) between the original interactions and their reconstructed ones. We test the model on a real-world dataset and evaluate it using Recall@k and NDCG@k. This work enhances the trustworthiness and accuracy of recommendation algorithms, mainly when deployed in social network analysis and mining, where the potential for malicious data manipulation is a critical concern.
Emoji are Effective Predictors of User’s Demographics

Social media platforms like Twitter provide rich data that can offer insights into various aspects of users' behavior. In this study, we explore the potential of emoji usage as a means for demographic prediction. Leveraging a Twitter dataset of 18,689 users, annotated with gender and ethnicity labels, we analyze the proportion of tweets containing emoji across different demographic groups. We identify significant variations in emoji usage, with women utilizing emoji more frequently than men and users of African descent displaying a higher tendency for emoji usage compared to users of European descent. Moreover, we investigate the most distinctive emoji for each group, revealing intriguing patterns that are closely tied to the cultural and demographic backgrounds of users. Building upon these findings, we employ machine learning models with different feature extraction techniques to predict users' gender and ethnicity. Our results demonstrate the predictive power of emoji, outperforming traditional text-based features. Furthermore, our study provides evidence that emoji usage can be a valuable resource for inferring user demographic characteristics on social media platforms, contributing to our understanding of user behavior in digital environments.
Enriching Wikipedia Texts through Geographic Information Extraction

Geographic Information Extraction (GIE) involves the extraction of geo-referenced information from a data collection through steps of geoparsing and geocoding. The former is a process that starts from a free textual description of locations with the goal of identifying an unambiguous location, such as specific geographic coordinates expressed as latitude-longitude. Differently, geocoding regards the easier task of translating an exact and well-formatted location such as postal addresses. This paper presents MAWI, i.e. a pipeline that starts from generic texts about cities that first extracts geographic information to automatically detect possible points of interest, then generates textual snippets from their contexts by means of Natural Language Processing (NLP) techniques. The adopted methodology involves several modules, ranging from publicly available geocoding systems to NLP libraries for Named Entity Recognition and text segmentation. The impact of the proposal includes multiple tasks and applications, e.g. i) the enrichment of public platforms of geographic data, ii) the detection of geographic scopes in textual documents, iii) a geo-centric exploration of locations in the tourism domain, and so forth. In this contribution, we present an experimentation of the system with 50 input Wikipedia pages referring different cities, first demonstrating its effectiveness with a running example, then evaluating its power to detect and structure a highly-significant amount of novel geo-referenced information with respect to what currently encoded in Wikipedia. Data and code are publicly available for future research at https://anonymous.4open.science/r/PointOfInterest-8D80/.
Measuring Online Emotional Reactions to Events

The rich and dynamic information environment of social media provides researchers, policy makers, and entrepreneurs with opportunities to learn about social phenomena in a timely manner. However, using this data to understand social behavior is difficult due heterogeneity of topics and events discussed in the highly dynamic online information environment. To address these challenges, we present a method for systematically detecting and measuring emotional reactions to offline events using change point detection on the time series of collective affect, and further explaining these reactions using a transformer-based topic model. We demonstrate the utility of the method on a corpus of tweets from a large US metropolitan area between January and August, 2020, covering a period of great social change. We demonstrate that our method is able to disaggregate topics to measure population's emotional and moral reactions. This capability allows for better monitoring of population's reactions during crises using online data.
The Impact of Linkability On Privacy Leakage

Online Social Networks are responsible for disclosing a large amount of sensitive information. Often, users unknowingly disclose vast amounts of sensitive and potentially (un)related data, oblivious to the associated privacy risks. Our research provides a comprehensive evaluation of the linkability between user profiles and shared content across various OSNs, a factor that has considerable implications for privacy leakage. We introduce a novel method for quantifying the linkability between profiles across multiple networks, based on key features and metrics that capture profile similarities. We applied this methodology to a dataset of user profiles across three online social networks named Flickr, Facebook, and Twitter. Our approach includes examining both structured and unstructured data related to user profiles, enabling us to offer a valuable understanding of linkability trends and identify potential privacy risks. Through our findings, we aim to inform the development of privacy-enhancing technologies and contribute to improving the current privacy landscape within OSNs. Our research underscores the critical need for robust privacy measures in the face of the growing interconnectedness of user data across different social networks.
Enhancing Automated Hate Speech Detection: Addressing Islamophobia and Freedom of Speech in Online Discussions

This paper emphasizes the necessity of a precise definition of Islamophobia within the realm of social media platforms. The current broad understanding often leads to misclassification and poses challenges to the principles of freedom of speech. Differentiating between Islamophobia and legitimate criticism presents a complex task for automated hate speech detection models, particularly in the presence of offensive language and emotionally charged tones. Furthermore, the paper highlights the inadvertent discriminatory consequences that can arise from misusing Islamophobia detection models against atheists, feminists, ex-Muslims, and others, underscoring the importance of safeguarding their rights. Our study introduces a refined definition and employs advanced deep learning models. It demonstrates a reduction in the number of Islamophobic comments in the dataset while maintaining the accurate identification of genuine instances of Islamophobia. This distinction is made without compromising discussions related to religion and criticism. The results show promise in improving the precision of Islamophobia identification, all while upholding principles of free expression and open dialogue.
Utilizing Textual Reviews for Visualizing and Understanding User Preferences

Latent factor models are widely used in recommender systems. In these models, users and items are represented as vectors in a joint latent factor space. The inner products of user vectors and item vectors are used to model the user-item interactions (e.g., ratings). A review is often posted by the user to explain the given rating. Therefore, reviews can be used to understand how users rate the items and to interpret the latent dimensions of user and item vectors. In this paper, we propose a probabilistic model that learns latent vectors of users and items in a two- or three-dimensional space for visualization. Our proposed model also extracts review topics and visualizes them in the same visualization space for interpreting the ratings. We model the user-item interactions by using the distances between users and items in the visualization space. Extensive experiments using several real-world datasets demonstrate the effectiveness of our proposed model in recommendation and visualization tasks.
Learning Common Knowledge Networks Via Exponential Random Graph Models

Common knowledge (CK) is a phenomenon where each individual within a group knows the same information and everyone knows that everyone knows the information, infinitely recursively. CK spreads information as a contagion through social networks in ways different from other models like susceptible-infectious-recovered (SIR) model. In a model of CK on Facebook, the biclique serves as the characterizing graph substructure for generating CK, as all nodes within a biclique share CK through their walls. To understand the effects of network structure on CK-based contagion, it is necessary to control the numbers and sizes of bicliques in networks. Thus, learning how to generate these CK networks (CKNs) is important. Consequently, we develop an exponential random graph model (ERGM) that constructs networks while controlling for bicliques. Our method offers powerful prediction and inference, reduces computational costs significantly, and has proven its merit in contagion dynamics through numerical experiments.
OntoFiC : an ontology for financial fraud detection and customer behavior modeling

Fraud detection is a complex issue for financial institutions. They must have tools for the prevention and detection of fraud. In this article, we present our approach to detect fraudulent transactions in SWIFT network based on the domain ontology. Firstly, we present the OntoFiC ontology constructed for the modeling of SWIFT transactions and actors. This ontology is populated with a real dataset. We developed our rules-based approach with rules associated to fraud scenarios to label our transactions as legitimate or fraudulent. Finally, we made SPARQL requests to visualize these transactions through graphs. Our work is part of a collaboration project with a financial company, SKAIZen Group.
Annotators' Perspectives: Exploring the Influence of Identity on Interpreting Misogynoir

Social Networking Sites are home to different forms of hate, including "Misogynoir", which specifically targets Black women through a combination of racism and sexism. Detecting misogynoir presents challenges due to its subjective nature and the varied interpretations of hate speech. Using annotator justifications from four distinct demographic groups; including Black women, Black men, White women and White men, we seek to gain a deeper understanding of the factors that influence annotators' reasoning process and labelling decisions for potential cases of Misogynoir and Allyship. Given the unique experiences of Black women who face both racism and sexism, the study sought to understand how their intersectional identities shape their perspectives compared to other groups. The research employed a qualitative analysis of responses from participants to identify key themes and patterns. Three significant themes emerged from our in-depth qualitative analysis of these annotator justifications: prior knowledge and experience, the language of the social media post, and its context. Our results revealed that annotators historically at risk of abuse demonstrated a nuanced understanding of how their intersecting identities inform their interpretations and judgement of tweets, drawing on their personal encounters with misogyny and racism compared to their non-target counterparts of this type of hate. This study underscores the significance of diverse annotator perspectives and content comprehension in understanding and addressing hate speech, particularly when it intersects with multiple forms of discrimination. Our study contributes to the methodological advancements in social network analysis and mining, highlighting the importance of considering annotator characteristics in the development of tools and approaches for detecting and addressing intersectional hate.
Personalized privacy-preserving semi-centralized recommendation system in a social network

In the contemporary era of big data, recommendation systems play a crucial role in guiding our daily decision-making amidst an overwhelming array of choices. Personalized recommendations have become increasingly popular by tailoring suggestions to user profiles, preferences, and interests. While many existing systems rely on centralizing data for making recommendations, the revelation of sensitive information poses a significant privacy concern. Studies indicate the potential to de-identify anonymous users, exposing details such as political views or sexual orientations through seemingly innocuous data, like movie ratings. In this paper, we introduce a personalized privacy-preserving semi-centralized recommendation system in a social network known as trust-based social network (TSN) to address these privacy challenges. TSN addresses privacy concerns by semi-centralizing data, treating each node in the network as an independent social entities. Data are distributed to social entities within trusted social networks, and the recommendation service provider only collects obfuscated data from social entities through the adoption of a differential-privacy mechanism. Consequently, data within TSN are either protected within local trusted social networks or obfuscated outside of these networks. The final recommendation is generated by combining local suggestions from the trusted social network with obfuscated global suggestions from the service provider. The emphasis on local suggestions ensures highly personalized recommendations. Evaluation results demonstrate that TSN achieves high accuracy in recommendations while effectively safeguarding user privacy.
Unexpected Attributed Subgraphs: a Mining Algorithm

Graphs are ubiquitous in real-world data, ranging from the study of social interactions to bioinformatics or the modelling of physical systems. These real-world graphs are typically sparse, possibly large and frequently contain additional information in the form of attributes, making them a complex object to understand. Graph summarization techniques can help facilitate the discovery of hidden patterns in underlying data by providing an interesting subset of the interactions and available attributes, which we broadly call a pattern. However, determining what is considered interesting in this context is not straightforward. We address this challenge by designing an interestingness measure based on the information-theoretic measure of Unexpectedness, linking the concepts of relevance and Kolmogorov complexity. We design a pattern mining algorithm to provide a summary of the initial data in the form of a set of unexpected patterns, that is, patterns for which there is a drop between their expected complexity and the observed complexity. Experimental results on five real-world datasets with state-of-the-art methods demonstrate that our method exhibits a small number of diversified patterns, providing a human-readable summary of the initial attributed graph; we show that our summaries quantitatively outperforms attribute-only and interaction-only baselines as well as other pattern mining methods, reinforcing the need for methods dealing with attributed graphs. We visualize summaries extracted with our method, in order to qualitatively validate their readability.
Learning Representations for Hyper-Relational Knowledge Graphs

Knowledge graphs (KGs) have gained prominence for their ability to learn representations for uni-relational facts. Recently, research has focused on modeling hyper-relational facts, which move beyond the restriction of uni-relational facts and allow us to represent more complex and real-world information. However, existing approaches for learning representations on hyper-relational KGs majorly focus on enhancing the communication from qualifiers to base triples while overlooking the flow of information from base triple to qualifiers. This can lead to suboptimal qualifier representations, especially when a large amount of qualifiers are presented. It motivates us to design a framework that utilizes multiple aggregators to learn representations for hyper-relational facts: one from the perspective of the base triple and the other one from the perspective of the qualifiers. Experiments demonstrate the effectiveness of our framework for hyper-relational knowledge graph completion across multiple datasets. Furthermore, we conduct an ablation study that validates the importance of the various components in our framework.
Emotion Mapping: Sentiment Analysis using Emoji in Twitter Data from Japan in the COVID-19 Era

This research explores the intricate relationship between emojis in Twitter interactions and sentiment interpretation in Japan. Emojis, as part of the digital language lexicon, have become essential in social media communication, especially on platforms like Twitter. However, integrating emojis into sentiment analysis has yet to be heavily explored, presenting an opportunity to better understand localized digital sentiment expression. This paper analyzes a data set within the COVID-19 Era of Twitter messages from Japan, focusing on emojis and the sentiments they convey. An established model was utilized to process Twitter text data and estimate their sentiment analysis, reflecting a more accurate portrayal of users' emotional states. We find a compelling link between emoji usage and the sentiment expressed. Emojis were shown to provide critical sentiment indicators often absent or ambiguous in the text alone. Certain emojis were identified as having a consistent correlation with particular sentiments.
Understanding and Analyzing COVID-19-related Online Hate Propagation Through Hateful Memes Shared on Twitter

Recent studies regarding the COVID-19 pandemic have revealed the widespread propagation of hateful content during this period. While significant research has focused on COVID-19-related online hate in text (e.g., text-based tweets), the role of memes in propagating online hate during the pandemic has been largely overlooked. Memes are a popular mechanism used by Internet users to convey their thoughts and opinions on a variety of topics. However, memes have emerged as an important mechanism through which ideologically potent and hateful content spreads on social media platforms. In this work, we focus on investigating the role of memes in the propagation of online hate during the COVID-19 pandemic. We first collect a novel dataset of 4,001 COVID-19-related hateful memes and their replies over a 3-year period from Twitter. Then, we carry out the first large-scale investigation into the impact of these memes on Twitter users, by studying the psychological reactions of Twitter users to these memes using various text analysis methods. We find that COVID-19-related hateful memes have a significantly greater negative impact on Twitter users in comparison to text-based hateful tweets, and increasing negativity towards such memes over the 3-year period. Our new dataset of COVID-19-related hateful memes and findings from our work pave the way for studying the dissemination and moderation of COVID-19-related online hate through the medium of memes.
FODGE - Fast Online Dynamic Graph Embedding

Graph embedding algorithms (GEA) project each vertex in a graph to a real-valued vector. Dynamic GEA (DGEA) are used to project dynamic graphs, where vertices and edges can appear and disappear. Such graphs are mostly divided into snapshots. Current DGEAs are often offline, and computationally expensive, and most do not ensure a slow change in vertices projection.
Quantifying customer interactions on ML optimized page layouts

In online businesses, personalization of site content is crucial for providing a better user experience and increasing customer engagement. Machine learning algorithms are often used to analyze customer data such as browsing behavior, purchase history to tailor the website content to each individual customer's preferences and needs. However, measuring the success of these personalized experiences can be challenging. While the ultimate goal is to convert customer visits into purchase sessions, tracking individual customer behavior can be difficult at scale. As a result, businesses often rely on aggregate metrics such as site-wide conversion rates, sales, and revenue to evaluate the effectiveness of their personalization efforts. However, it's important to understand individual customers' experiences with these ML-optimized pages. To address this, we propose a supervised ML model that quantifies customer engagement while browsing auto-optimized web pages, by building a customer site interaction score (CSI score). We first introduce a novel representation of customer click logs as a tree data structure induced by the webpage's DOM structure. Then, we propose a novel attention model on the tree structure that performs vertical attention across the depth of the tree and horizontal attention across the sequence of trees to summarize customer interactions. The effectiveness of the proposed approach is evaluated using click logs obtained from the e-commerce domain.
Realtime Disaster Detection Through GNN Models Using Disaster Knowledge Graphs

In the context of the increasing scale and complexity of disasters caused by rapid climate change, a comprehensive understanding of disaster big data is essential for effective detection and response. The disaster knowledge graph proposed in this paper fills this gap by capturing the connections between various disaster-related data sources and their potential for growth across heterogeneous datasets. We generate time-series disaster graphs every minute using SNS data (e.g., Twitter) and public data, specifically focusing on disasters. Then, we create disaster knowledge graphs to represent the relationships between various data sources and try to predict their potential developments. We label and annotate knowledge graphs and then detect sudden changes in time-series disaster knowledge graphs for disaster detection. To that end, we assess the effectiveness of three state-of-the-art GNN models for graph-based event classification using Graph Convolutional Network (GCN), Graph Attention Network (GAT), and SageConv. In addition, we evaluate a simple clustering model, K-means, for comparison. Our experiments show promising results with approximately 87% precision in detecting disaster events using structural data and connectivity patterns within disaster graphs. Finally, we measure the result of disaster detection time with an unseen dataset, showing positive results that about 70% detect a disaster in less than 3 minutes. To comprehensively analyze real-time social media data and understand the patterns of disaster to enhance disaster management and response strategies, our approach combines the strength of GNNs with a designed disaster knowledge graph.