Archival Circulation on the Web: The Vine-Tweets Dataset

Summers, Edward; Wickner, Amy

doi:10.31235/osf.io/6p8ea

Cited by 2 publications

(2 citation statements)

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“…For each emoji selected, we collected 500 tweets every day for the time period between 01-01-2020 to 01-01-2023. In total 7,379,453 new tweets were gathered through the Twitter API utilising the Twarc library (Summers, 2013).…”

Section: Emoji Prediction (Tweetemoji100)mentioning

confidence: 99%

SuperTweetEval: A Challenging, Unified and Heterogeneous Benchmark for Social Media NLP Research

Antypas,

Ushio,

Barbieri

et al. 2023

Findings of the Association for Computational Linguistics: EMNLP 2023

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Despite its relevance, the maturity of NLP for social media pales in comparison with generalpurpose models, metrics and benchmarks. This fragmented landscape makes it hard for the community to know, for instance, given a task, which is the best performing model and how it compares with others. To alleviate this issue, we introduce a unified benchmark for NLP evaluation in social media, SUPERTWEETE-VAL, which includes a heterogeneous set of tasks and datasets combined, adapted and constructed from scratch. We benchmarked the performance of a wide range of models on SU-PERTWEETEVAL and our results suggest that, despite the recent advances in language modelling, social media remains challenging.

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Section: Emoji Prediction (Tweetemoji100)mentioning

confidence: 99%

SuperTweetEval: A Challenging, Unified and Heterogeneous Benchmark for Social Media NLP Research

Antypas,

Ushio,

Barbieri

et al. 2023

Findings of the Association for Computational Linguistics: EMNLP 2023

View full text Add to dashboard Cite

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“…Video can be challenging to edit without appropriate training and software, and much more challenging to update compared to text-based materials. Although social media platforms now focus on video distribution, such as TikTok and Instagram, other older formats are no longer readily available, with the primary example of VINE now archived and videos no longer easily accessible (Summers & Wickner, 2019 ).…”

Section: Introductionmentioning

confidence: 99%

The Triple-S framework: ensuring scalable, sustainable, and serviceable practices in educational technology

Moro

Mills

Phelps

et al. 2023

Int J Educ Technol High Educ

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Educational institutions are increasingly investing into digital delivery, acquiring new devices, and employing novel software and services. The rising costs associated with maintenance, in combination with increasing redundancy of older technologies, presents multiple challenges. While lesson content itself may not have changed, the educational landscape constantly evolves, where tertiary institutions are incorporating new modes of content delivery, hybrid-style learning, and interactive technologies. Investments into digital expansions must be taken with caution, particularly prior to the procurement of technology, with a need for the proposed interventions’ scalability, sustainability, and serviceability to be considered. This article presents the Triple-S framework for educators, administrators, and educational institutions, and outlines examples of its application within curricula. The paper synthesises research evidence to provide the foundation underlying the key principles of the Triple-S framework, presenting a useful model to use when evaluating digital interventions. Utilising the framework for decisions regarding the acquisition of educational technology, devices, software, applications, and online resources can assist in the assurance of viable and appropriate investments. Graphical Abstract

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Archival Circulation on the Web: The Vine-Tweets Dataset

Cited by 2 publications

References 0 publications

SuperTweetEval: A Challenging, Unified and Heterogeneous Benchmark for Social Media NLP Research

SuperTweetEval: A Challenging, Unified and Heterogeneous Benchmark for Social Media NLP Research

The Triple-S framework: ensuring scalable, sustainable, and serviceable practices in educational technology

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