# Social Analysis of Young Basque Speaking Communities in Twitter

arXiv: https://arxiv.org/abs/2109.03487 · HTML: https://mlsanity.com/paper/2109.03487/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 46 references that are in the map.
- First posted on arXiv 8 Sep 2021.

## Builds on (most cited first, 17 of 17)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [node2vec: Scalable Feature Learning for Networks](https://mlsanity.com/paper/1607.00653/) (2016)
- [Enriching Word Vectors with Subword Information](https://mlsanity.com/paper/1607.04606/) (2016)
- [SemEval-2017 Task 4: Sentiment Analysis in Twitter](https://mlsanity.com/paper/1912.00741/) (2019)
- [Cross-Lingual Ability of Multilingual BERT: An Empirical Study](https://mlsanity.com/paper/1912.07840/) (2019)
- [SemEval-2017 Task 8: RumourEval: Determining rumour veracity and support for rumours](https://mlsanity.com/paper/1704.05972/) (2017)
- [Computational Sociolinguistics: A Survey](https://mlsanity.com/paper/1508.07544/) (2015)
- [Give your Text Representation Models some Love: the Case for Basque](https://mlsanity.com/paper/2004.00033/) (2020)
- [Arap-Tweet: A Large Multi-Dialect Twitter Corpus for Gender, Age and Language Variety Identification](https://mlsanity.com/paper/1808.07674/) (2018)
- [Semi-automatic Generation of Multilingual Datasets for Stance Detection in Twitter](https://mlsanity.com/paper/2101.11978/) (2021)
- [Robust Multilingual Named Entity Recognition with Shallow Semi-Supervised Features](https://mlsanity.com/paper/1701.09123/) (2017)
- [Language Independent Sequence Labelling for Opinion Target Extraction](https://mlsanity.com/paper/1901.09755/) (2019)
- [How well can machine learning predict demographics of social media users?](https://mlsanity.com/paper/1702.01807/) (2017)
- [Political Homophily in Independence Movements: Analysing and Classifying Social Media Users by National Identity](https://mlsanity.com/paper/1702.08388/) (2017)

## Built on by (most cited first, 4 of 4)

- [Optimal strategies to perform multilingual analysis of social content for a novel dataset in the tourism domain](https://mlsanity.com/paper/2311.14727/) (2023)
- [Political Leaning Inference through Plurinational Scenarios](https://mlsanity.com/paper/2406.07964/) (2024)
- [BERnaT: Basque Encoders for Representing Natural Textual Diversity](https://mlsanity.com/paper/2512.03903/) (2025)
- [A Catalog of Basque Dialectal Resources: Online Collections and Standard-to-Dialectal Adaptations](https://mlsanity.com/paper/2603.25189/) (2026)

## Abstract (arXiv)

In this paper we take into account both social and linguistic aspects to perform demographic analysis by processing a large amount of tweets in Basque language. The study of demographic characteristics and social relationships are approached by applying machine learning and modern deep-learning Natural Language Processing (NLP) techniques, combining social sciences with automatic text processing. More specifically, our main objective is to combine demographic inference and social analysis in order to detect young Basque Twitter users and to identify the communities that arise from their relationships or shared content. This social and demographic analysis will be entirely based on the~automatically collected tweets using NLP to convert unstructured textual information into interpretable knowledge.