Changes in European Solidarity Before and During COVID-19: Evidence from a Large Crowd- and Expert-Annotated Twitter Dataset

• URL: http://arxiv.org/abs/2108.01042v1
• Date: Mon, 2 Aug 2021 17:03:12 GMT
• Title: Changes in European Solidarity Before and During COVID-19: Evidence from a Large Crowd- and Expert-Annotated Twitter Dataset
• Authors: Alexandra Ils and Dan Liu and Daniela Grunow and Steffen Eger
• Abstract summary: We introduce the well-established social scientific concept of social solidarity and its contestation, anti-solidarity, as a new problem setting to supervised machine learning in NLP. We annotate 2.3k English and German tweets for (anti-)solidarity expressions, utilizing multiple human annotators and two annotation approaches (experts vs. crowds) Our results show that solidarity became increasingly salient and contested during the COVID-19 crisis.
• Score: 77.27709662210363
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We introduce the well-established social scientific concept of social solidarity and its contestation, anti-solidarity, as a new problem setting to supervised machine learning in NLP to assess how European solidarity discourses changed before and after the COVID-19 outbreak was declared a global pandemic. To this end, we annotate 2.3k English and German tweets for (anti-)solidarity expressions, utilizing multiple human annotators and two annotation approaches (experts vs.\ crowds). We use these annotations to train a BERT model with multiple data augmentation strategies. Our augmented BERT model that combines both expert and crowd annotations outperforms the baseline BERT classifier trained with expert annotations only by over 25 points, from 58\% macro-F1 to almost 85\%. We use this high-quality model to automatically label over 270k tweets between September 2019 and December 2020. We then assess the automatically labeled data for how statements related to European (anti-)solidarity discourses developed over time and in relation to one another, before and during the COVID-19 crisis. Our results show that solidarity became increasingly salient and contested during the crisis. While the number of solidarity tweets remained on a higher level and dominated the discourse in the scrutinized time frame, anti-solidarity tweets initially spiked, then decreased to (almost) pre-COVID-19 values before rising to a stable higher level until the end of 2020.

Related papers

• Understanding writing style in social media with a supervised contrastively pre-trained transformer [57.48690310135374]
  Online Social Networks serve as fertile ground for harmful behavior, ranging from hate speech to the dissemination of disinformation. We introduce the Style Transformer for Authorship Representations (STAR), trained on a large corpus derived from public sources of 4.5 x 106 authored texts. Using a support base of 8 documents of 512 tokens, we can discern authors from sets of up to 1616 authors with at least 80% accuracy.
  arXiv  Detail & Related papers  (2023-10-17T09:01:17Z)
• CTC-based Non-autoregressive Speech Translation [51.37920141751813]
  We investigate the potential of connectionist temporal classification for non-autoregressive speech translation. We develop a model consisting of two encoders that are guided by CTC to predict the source and target texts. Experiments on the MuST-C benchmarks show that our NAST model achieves an average BLEU score of 29.5 with a speed-up of 5.67$times$.
  arXiv  Detail & Related papers  (2023-05-27T03:54:09Z)
• Fine-Grained Detection of Solidarity for Women and Migrants in 155 Years of German Parliamentary Debates [16.382860418871804]
  We study solidarity towards women and migrants in German parliamentary debates between 1867 and 2022. Using 2,864 manually annotated text snippets, we evaluate large language models (LLMs) like Llama 3, GPT-3.5, and GPT-4. Using GPT-4, we automatically annotate more than 18k further instances (with a cost of around 500 Euro) across 155 years.
  arXiv  Detail & Related papers  (2022-10-09T22:02:58Z)
• Twitter conversations predict the daily confirmed COVID-19 cases [0.2320417845168326]
  Pandemic-specific discourse has remained on-trend on microblogging platforms such as Twitter and Weibo. We propose a sentiment-involved topic-based methodology for designing multiple time series from publicly available COVID-19 related Twitter conversations. We show that the inclusion of social media variables for modeling introduces 48.83--51.38% improvements on RMSE over the baseline models.
  arXiv  Detail & Related papers  (2022-06-21T15:31:06Z)
• COVID-19 Tweets Analysis through Transformer Language Models [0.0]
  In this study, we perform an in-depth, fine-grained sentiment analysis of tweets in COVID-19. A trained transformer model is able to correctly predict, with high accuracy, the tone of a tweet. We then leverage this model for predicting tones for 200,000 tweets on COVID-19.
  arXiv  Detail & Related papers  (2021-02-27T12:06:33Z)
• Learning from History for Byzantine Robust Optimization [52.68913869776858]
  Byzantine robustness has received significant attention recently given its importance for distributed learning. We show that most existing robust aggregation rules may not converge even in the absence of any Byzantine attackers.
  arXiv  Detail & Related papers  (2020-12-18T16:22:32Z)
• Robust Pre-Training by Adversarial Contrastive Learning [120.33706897927391]
  Recent work has shown that, when integrated with adversarial training, self-supervised pre-training can lead to state-of-the-art robustness. We improve robustness-aware self-supervised pre-training by learning representations consistent under both data augmentations and adversarial perturbations.
  arXiv  Detail & Related papers  (2020-10-26T04:44:43Z)
• Not-NUTs at W-NUT 2020 Task 2: A BERT-based System in Identifying Informative COVID-19 English Tweets [0.0]
  We propose a model that, given an English tweet, automatically identifies whether that tweet bears informative content regarding COVID-19 or not. We have achieved competitive results that are only shy of those by top performing teams by roughly 1% in terms of F1 score on the informative class.
  arXiv  Detail & Related papers  (2020-09-14T15:49:16Z)
• SenWave: Monitoring the Global Sentiments under the COVID-19 Pandemic [26.109661374693935]
  We introduce SenWave, a novel sentimental analysis work using 105+ million collected tweets and Weibo messages. SenWave reveals the sentiment of global conversation in six different languages on COVID-19. Overall, SenWave shows that optimistic and positive sentiments increased over time, foretelling a desire to seek, together, a reset for an improved COVID-19 world.
  arXiv  Detail & Related papers  (2020-06-18T20:33:41Z)
• Cross-lingual Transfer Learning for COVID-19 Outbreak Alignment [90.12602012910465]
  We train on Italy's early COVID-19 outbreak through Twitter and transfer to several other countries. Our experiments show strong results with up to 0.85 Spearman correlation in cross-country predictions.
  arXiv  Detail & Related papers  (2020-06-05T02:04:25Z)

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.