Multi-Source Social Feedback of Online News Feeds
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Abstract (arXiv)
The profusion of user generated content caused by the rise of social media platforms has enabled a surge in research relating to fields such as information retrieval, recommender systems, data mining and machine learning. However, the lack of comprehensive baseline data sets to allow a thorough evaluative comparison has become an important issue. In this paper we present a large data set of news items from well-known aggregators such as Google News and Yahoo! News, and their respective social feedback on multiple platforms: Facebook, Google+ and LinkedIn. The data collected relates to a period of 8 months, between November 2015 and July 2016, accounting for about 100,000 news items on four different topics: economy, microsoft, obama and palestine. This data set is tailored for evaluative comparisons in predictive analytics tasks, although allowing for tasks in other research areas such as topic detection and tracking, sentiment analysis in short text, first story detection or news recommendation.
Key facts. Cited by 14 papers in the map (data through 7 Oct 2026). First posted on arXiv 22 Jan 2018.
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Citing papers per year: 2022: 4; 2023: 4; 2024: 2; 2025: 4.
14 papers.
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- 2022Demystifying Prompts in Language Models via Perplexity Estimationcited by 215
- 2023Goal Driven Discovery of Distributional Differences via Language Descriptionscited by 61
- 2022Integrating Random Effects in Deep Neural Networkscited by 22
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- 2024Monotonic Paraphrasing Improves Generalization of Language Model Promptingcited by 10
- 2023Neural Mixed Effects for Nonlinear Personalized Predictionscited by 5
- 2025Unveiling the Potential of Text in High-Dimensional Time Series Forecastingcited by 4
- 2022Flexible Modeling and Multitask Learning using Differentiable Tree Ensemblescited by 3
- 2023OEBench: Investigating Open Environment Challenges in Real-World Relational Data Streamscited by 3
- 2025MoTime: A Dataset Suite for Multimodal Time Series Forecastingcited by 3
- 2024Integrating Random Effects in Variational Autoencoders for Dimensionality Reduction of Correlated Datacited by 1
- 2025The Ripple Effect: On Unforeseen Complications of Backdoor Attackscited by 1
- 2025MMbeddings: Parameter-Efficient, Low-Overfitting Probabilistic Embeddings Inspired by Nonlinear Mixed Models25 Oct 2025
- 2025MoTime: A Dataset Suite for Multimodal Time Series Forecasting21 May 2025
- 2025The Ripple Effect: On Unforeseen Complications of Backdoor Attacks16 May 2025
- 2025Unveiling the Potential of Text in High-Dimensional Time Series Forecasting13 Jan 2025
- 2024Integrating Random Effects in Variational Autoencoders for Dimensionality Reduction of Correlated Data22 Dec 2024
- 2024Monotonic Paraphrasing Improves Generalization of Language Model Prompting24 Mar 2024
- 2023OEBench: Investigating Open Environment Challenges in Real-World Relational Data Streams29 Aug 2023
- 2023Neural Mixed Effects for Nonlinear Personalized Predictions13 Jun 2023
- 2023Improving Probability-based Prompt Selection Through Unified Evaluation and Analysis24 May 2023
- 2023Goal Driven Discovery of Distributional Differences via Language Descriptions28 Feb 2023
- 2022Towards Proactively Forecasting Sentence-Specific Information Popularity within Online News Documents31 Dec 2022
- 2022Demystifying Prompts in Language Models via Perplexity Estimation8 Dec 2022
2 more
- 2022Demystifying Prompts in Language Models via Perplexity Estimationcited by 215
- 2023Goal Driven Discovery of Distributional Differences via Language Descriptionscited by 61
- 2022Integrating Random Effects in Deep Neural Networkscited by 22
- 2023Improving Probability-based Prompt Selection Through Unified Evaluation and Analysiscited by 13
- 2024Monotonic Paraphrasing Improves Generalization of Language Model Promptingcited by 10
- 2023Neural Mixed Effects for Nonlinear Personalized Predictionscited by 5
- 2025Unveiling the Potential of Text in High-Dimensional Time Series Forecastingcited by 4
- 2022Flexible Modeling and Multitask Learning using Differentiable Tree Ensemblescited by 3
- 2023OEBench: Investigating Open Environment Challenges in Real-World Relational Data Streamscited by 3
- 2025MoTime: A Dataset Suite for Multimodal Time Series Forecastingcited by 3
- 2024Integrating Random Effects in Variational Autoencoders for Dimensionality Reduction of Correlated Datacited by 1
- 2025The Ripple Effect: On Unforeseen Complications of Backdoor Attackscited by 1
12 most cited of 14 All 14 (JSON)
- 2025MMbeddings: Parameter-Efficient, Low-Overfitting Probabilistic Embeddings Inspired by Nonlinear Mixed Models25 Oct 2025
- 2025MoTime: A Dataset Suite for Multimodal Time Series Forecasting21 May 2025
- 2025The Ripple Effect: On Unforeseen Complications of Backdoor Attacks16 May 2025
- 2025Unveiling the Potential of Text in High-Dimensional Time Series Forecasting13 Jan 2025
- 2024Integrating Random Effects in Variational Autoencoders for Dimensionality Reduction of Correlated Data22 Dec 2024
- 2024Monotonic Paraphrasing Improves Generalization of Language Model Prompting24 Mar 2024
- 2023OEBench: Investigating Open Environment Challenges in Real-World Relational Data Streams29 Aug 2023
- 2023Neural Mixed Effects for Nonlinear Personalized Predictions13 Jun 2023
- 2023Improving Probability-based Prompt Selection Through Unified Evaluation and Analysis24 May 2023
- 2023Goal Driven Discovery of Distributional Differences via Language Descriptions28 Feb 2023
- 2022Towards Proactively Forecasting Sentence-Specific Information Popularity within Online News Documents31 Dec 2022
- 2022Demystifying Prompts in Language Models via Perplexity Estimation8 Dec 2022
12 newest of 14 All 14 (JSON)