Multilingual use of Twitter: Social networks at the language frontier

Eleta, Irene; Golbeck, Jennifer

doi:10.1016/j.chb.2014.05.005

Cited by 66 publications

(52 citation statements)

References 7 publications

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“…If a dominant group can be revealed, then one of the languages becomes peripheral [Eleta, Golbeck, 2014]. According to their findings, the cross-language edge ratio, that is, the number of edges linking two nodes of different languages against the total number of edges in the graph, 'could reflect the potential for information dissemination across language borders' [Eleta, Golbeck, 2014]. Other studies have also shown that language is not always a barrier.…”

Section: The Global Public Sphere On Twittermentioning

confidence: 99%

“…In case of separated language groups on Twitter, a few gatekeeper accounts may connect two separated language groups, while a bigger language bridge occurs in case when two language groups of similar size are tightly connected. If a dominant group can be revealed, then one of the languages becomes peripheral [Eleta, Golbeck, 2014]. According to their findings, the cross-language edge ratio, that is, the number of edges linking two nodes of different languages against the total number of edges in the graph, 'could reflect the potential for information dissemination across language borders' [Eleta, Golbeck, 2014].…”

Section: The Global Public Sphere On Twittermentioning

confidence: 99%

“…Later, Eleta and Golbeck [Eleta, Golbeck, 2014] defined three types of bilingual networks among Twitter users. In case of separated language groups on Twitter, a few gatekeeper accounts may connect two separated language groups, while a bigger language bridge occurs in case when two language groups of similar size are tightly connected.…”

Section: The Global Public Sphere On Twittermentioning

confidence: 99%

See 2 more Smart Citations

A global public sphere of compassion? #JeSuisCharlie and #JeNeSuisPasCharlie on Twitter and their language boundaries

Bodrunova¹,

Smoliarova²,

Blekanov³

et al. 2018

THE MONITORING OF PUBLIC OPINION

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Section: The Global Public Sphere On Twittermentioning

confidence: 99%

Section: The Global Public Sphere On Twittermentioning

confidence: 99%

Section: The Global Public Sphere On Twittermentioning

confidence: 99%

See 1 more Smart Citation

A global public sphere of compassion? #JeSuisCharlie and #JeNeSuisPasCharlie on Twitter and their language boundaries

Bodrunova¹,

Smoliarova²,

Blekanov³

et al. 2018

THE MONITORING OF PUBLIC OPINION

View full text Add to dashboard Cite

“…The majority of work in social media focused on word-level language identification (Solorio et al, 2014;Jain and Bhat, 2014) and automatic prediction of code-switching points (Nguyen and Dogruöz, 2013). Other studies investigated how language groups connect within a network of multilingual users (Eleta and Golbeck, 2014;Kim et al, 2014), the use of codeswitched hashtags (Jurgens et al, 2014) and minority languages on Twitter (Nguyen et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

“…Twitter is known for its diverse multi-cultural and multilingual interactions (Mocanu et al, 2013) where multilingual users play an important bridging role in global social network connectivity (Hale, 2014;Eleta and Golbeck, 2014).…”

Section: Introductionmentioning

confidence: 99%

Predicting Foreign Language Usage from English-Only Social Media Posts

Volkova

Ranshous²,

Phillips

2018

Proceedings of the 2018 Conference of the North American Chapter Of the Association for Computational Linguistics: Hu

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Social media is known for its multi-cultural and multilingual interactions, a natural product of which is code-mixing. Multilingual speakers mix languages they tweet to address a different audience, express certain feelings, or attract attention. This paper presents a largescale analysis of 6 million tweets produced by 27 thousand multilingual users speaking 12 other languages besides English. We rely on this corpus to build predictive models to infer non-English languages that users speak exclusively from their English tweets. Unlike native language identification task, we rely on large amounts of informal social media communications rather than ESL essays. We contrast the predictive power of the state-of-theart machine learning models trained on lexical, syntactic, and stylistic signals with neural network models learned from word, character and byte representations extracted from English only tweets. We report that content, style and syntax are the most predictive of nonEnglish languages that users speak on Twitter. Neural network models learned from byte representations of user content combined with transfer learning yield the best performance. Finally, by analyzing cross-lingual transferthe influence of non-English languages on various levels of linguistic performance in English, we present novel findings on stylistic and syntactic variations across speakers of 12 languages in social media.

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