Privacy-Aware Recommender Systems Challenge on Twitter's Home Timeline

• URL: http://arxiv.org/abs/2004.13715v3
• Date: Wed, 7 Oct 2020 13:25:23 GMT
• Title: Privacy-Aware Recommender Systems Challenge on Twitter's Home Timeline
• Authors: Luca Belli, Sofia Ira Ktena, Alykhan Tejani, Alexandre Lung-Yut-Fong, Frank Portman, Xiao Zhu, Yuanpu Xie, Akshay Gupta, Michael Bronstein, Amra Deli\'c, Gabriele Sottocornola, Walter Anelli, Nazareno Andrade, Jessie Smith, Wenzhe Shi
• Abstract summary: RecSys 2020 Challenge organized by ACM RecSys in partnership with Twitter using this dataset. This paper touches on the key challenges faced by researchers and professionals striving to predict user engagements.
• Score: 47.434392695347924
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recommender systems constitute the core engine of most social network platforms nowadays, aiming to maximize user satisfaction along with other key business objectives. Twitter is no exception. Despite the fact that Twitter data has been extensively used to understand socioeconomic and political phenomena and user behaviour, the implicit feedback provided by users on Tweets through their engagements on the Home Timeline has only been explored to a limited extent. At the same time, there is a lack of large-scale public social network datasets that would enable the scientific community to both benchmark and build more powerful and comprehensive models that tailor content to user interests. By releasing an original dataset of 160 million Tweets along with engagement information, Twitter aims to address exactly that. During this release, special attention is drawn on maintaining compliance with existing privacy laws. Apart from user privacy, this paper touches on the key challenges faced by researchers and professionals striving to predict user engagements. It further describes the key aspects of the RecSys 2020 Challenge that was organized by ACM RecSys in partnership with Twitter using this dataset.

Related papers

• Cross-Network Social User Embedding with Hybrid Differential Privacy Guarantees [81.6471440778355]
  We propose a Cross-network Social User Embedding framework, namely DP-CroSUE, to learn the comprehensive representations of users in a privacy-preserving way. In particular, for each heterogeneous social network, we first introduce a hybrid differential privacy notion to capture the variation of privacy expectations for heterogeneous data types. To further enhance user embeddings, a novel cross-network GCN embedding model is designed to transfer knowledge across networks through those aligned users.
  arXiv  Detail & Related papers  (2022-09-04T06:22:37Z)
• Retweet-BERT: Political Leaning Detection Using Language Features and Information Diffusion on Social Networks [30.143148646797265]
  We introduce Retweet-BERT, a simple and scalable model to estimate the political leanings of Twitter users. Our assumptions stem from patterns of networks and linguistics homophily among people who share similar ideologies.
  arXiv  Detail & Related papers  (2022-07-18T02:18:20Z)
• A deep dive into the consistently toxic 1% of Twitter [9.669275987983447]
  This study spans 14 years of tweets from 122K Twitter profiles and more than 293M tweets. We selected the most extreme profiles in terms of consistency of toxic content and examined their tweet texts, and the domains, hashtags, and URLs they shared. We found that these selected profiles keep to a narrow theme with lower diversity in hashtags, URLs, and domains, they are thematically similar to each other, and have a high likelihood of bot-like behavior.
  arXiv  Detail & Related papers  (2022-02-16T04:21:48Z)
• Twitter-Demographer: A Flow-based Tool to Enrich Twitter Data [31.19059013571499]
  Twitter-Demographer is a flow-based tool to enrich Twitter data with additional information about tweets and users. We discuss our design choices, inspired by the flow-based programming paradigm, to use black-box components that can easily be chained together and extended.
  arXiv  Detail & Related papers  (2022-01-26T14:59:17Z)
• Identification of Twitter Bots based on an Explainable ML Framework: the US 2020 Elections Case Study [72.61531092316092]
  This paper focuses on the design of a novel system for identifying Twitter bots based on labeled Twitter data. Supervised machine learning (ML) framework is adopted using an Extreme Gradient Boosting (XGBoost) algorithm. Our study also deploys Shapley Additive Explanations (SHAP) for explaining the ML model predictions.
  arXiv  Detail & Related papers  (2021-12-08T14:12:24Z)
• Federated Social Recommendation with Graph Neural Network [69.36135187771929]
  We propose fusing social information with user-item interactions to alleviate it, which is the social recommendation problem. We devise a novel framework textbfFedrated textbfSocial recommendation with textbfGraph neural network (FeSoG)
  arXiv  Detail & Related papers  (2021-11-21T09:41:39Z)
• SOK: Seeing and Believing: Evaluating the Trustworthiness of Twitter Users [4.609388510200741]
  Currently, there is no automated way of determining which news or users are credible and which are not. In this work, we created a model which analysed the behaviour of50,000 politicians on Twitter. We classified the political Twitter users as either trusted or untrusted using random forest, multilayer perceptron, and support vector machine.
  arXiv  Detail & Related papers  (2021-07-16T17:39:32Z)
• News consumption and social media regulations policy [70.31753171707005]
  We analyze two social media that enforced opposite moderation methods, Twitter and Gab, to assess the interplay between news consumption and content regulation. Our results show that the presence of moderation pursued by Twitter produces a significant reduction of questionable content. The lack of clear regulation on Gab results in the tendency of the user to engage with both types of content, showing a slight preference for the questionable ones which may account for a dissing/endorsement behavior.
  arXiv  Detail & Related papers  (2021-06-07T19:26:32Z)
• Birdspotter: A Tool for Analyzing and Labeling Twitter Users [12.558187319452657]
  Birdspotter is a tool to analyze and label Twitter users. Birdspotter.ml is an exploratory visualizer for the computed metrics. We show how to train birdspotter into a fully-fledged bot detector.
  arXiv  Detail & Related papers  (2020-12-04T02:25:07Z)
• Study of the usability of LinkedIn: a social media platform meant to connect employers and employees [91.3755431537592]
  This paper is assessing LinkedIn's usability using both user and expert evaluation. The overall usability of LinkedIn application has been measured by using SUS (System Usability Scale)
  arXiv  Detail & Related papers  (2020-06-06T18:19:45Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.