Who talks about what? Comparing the information treatment in traditional media with online discussions

@article{Schawe2022WhoTA,
  title={Who talks about what? Comparing the information treatment in traditional media with online discussions},
  author={Hendrik Schawe and Mariano G. Beir'o and Jos{\'e} Ignacio Alvarez-Hamelin and Dimitris Kotzinos and Laura Hern'andez},
  journal={ArXiv},
  year={2022},
  volume={abs/2208.14393}
}
We study the dynamics of interactions between a traditional medium, the New York Times journal, and its followers in Twitter, using a massive dataset. It consists of the metadata of the articles published by the journal during the first year of the COVID-19 pandemic, and the posts published in Twitter by a large set of followers of the @nytimes account along with those published by a set of followers of several other media of different kind. The dynamics of discussions held in Twitter by… 

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References

SHOWING 1-10 OF 42 REFERENCES

The echo chamber effect on social media

A comparative analysis of more than 100 million pieces of content concerning several controversial topics from Gab, Facebook, Reddit, and Twitter shows that the aggregation of users in homophilic clusters dominate online interactions on Facebook and Twitter.

Twitter and the Traditional Media: Who is the Real Agenda Setter?

The rise of social media and social network sites has re-opened the debate on the role of Internet as an ‘uncoerced’ public sphere that provides room for (direct) e-democracy and deliberation through

What’s in Twitter, I know what parties are popular and who you are supporting now!

An incremental and practical classification method which uses the number of Twitter messages referring to a particular political party or retweets, and a classifier leveraging the valuable semantic content of the List feature to estimate the overall political leaning of users is developed.

Evolution of the political opinion landscape during electoral periods

A semantic network based on the hashtags used by all the users following at least one of the main candidates can capture the reshaping of the political opinion landscape which has led to the inversion of result between the two rounds of 2015 election.

Reconstruction of the socio-semantic dynamics of political activist Twitter networks—Method and application to the 2017 French presidential election

It is demonstrated that social networks data make it possible to qualify and quantify the activity of political communities in a multi-polar political environment; as well as their temporal evolution and reconfiguration, their structure, their alliance strategies and their semantic particularities during a presidential campaign through the analysis of their digital traces.

Attention dynamics on the Chinese social media Sina Weibo during the COVID-19 pandemic

This work focuses on how COVID-19 has influenced the attention dynamics on the biggest Chinese microblogging website Sina Weibo during the first four months of the pandemic, and explores the dynamics of HSL by measuring the ranking dynamics and the lifetimes of hashtags on the list.

Tweeting Apart: Applying Network Analysis to Detect Selective Exposure Clusters in Twitter

This work collected networks of connections among users who talked about a shared topic: the U.S. President's State of the Union speech in 2012 and developed a Selective Exposure Cluster (SEC) method to study these connected networks and their discussion patterns in Twitter.

Measuring Online Social Bubbles

There is a strong correlation between collective and individual diversity, supporting the notion that when the authors use social media they find ourselves inside “social bubbles,” and could lead to a deeper understanding of how technology biases their exposure to new information.

Infodemic Pathways: Evaluating the Role That Traditional and Social Media Play in Cross-National Information Transfer

The COVID-19 pandemic has occurred alongside a worldwide infodemic where unprecedented levels of misinformation have contributed to widespread misconceptions about the novel coronavirus. Conspiracy