Evolution of the political opinion landscape during electoral periods

Reyero, Tomás Mussi; Beiró, Mariano G.; Alvarez-Hamelin, José Ignacio; Hernández, Laura; Kotzinos, Dimitris

doi:10.1140/epjds/s13688-021-00285-8

Cited by 8 publications

(4 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A common strategy to follow the discussion about a topic is to pre-select the hashtags that are supposed to be related to the topic. Here we use a different approach where the topics emerge from a semantic network of hashtags 37 , 61 . The vertices of this network are the hashtags found in our dataset, and the weighted edge between two nodes represents the number of different users that used those hashtags together in at least one of their tweets.…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Understanding who talks about what: comparison between the information treatment in traditional media and online discussions

Schawe

Beiró²,

Alvarez-Hamelin³

et al. 2023

Sci Rep

Self Cite

View full text Add to dashboard Cite

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 exclusive followers of a medium show a strong dependence on the medium they follow: the followers of @FoxNews show the highest similarity to each other and a strong differentiation of interests with the general group. Our results also reveal the difference in the attention payed to U.S. presidential elections by the journal and by its followers, and show that the topic related to the “Black Lives Matter” movement started in Twitter, and was addressed later by the journal.

show abstract

Section: Methodsmentioning

confidence: 99%

“…To study how similar users are in regard to the interest they pay to different topics, we applied the method used in 61 . We describe the interests of each user i by means of a user description vector of dimension , the number of topics (communities) found, which informs about the topic preferences of user i .…”

Section: Methodsmentioning

confidence: 99%

Understanding who talks about what: comparison between the information treatment in traditional media and online discussions

Schawe

Beiró²,

Alvarez-Hamelin³

et al. 2023

Sci Rep

Self Cite

View full text Add to dashboard Cite

show abstract

“…The reconstruction of opinion spaces from SNSs data has been a very active field of research these last several years, with reconstructions in one (Barberá 2015;Briatte & Gallic 2015), two dimensional spaces (Gaumont et al 2018;Chomel et al 2022) or even in spaces with variable dimensions (Reyero et al 2021). As for retweet networks, retweeting someone on a recurring basis has been demonstrated to be an indicator to some ideological alignment (Garimella et al 2018;Conover et al 2011;Gaumont et al 2018).…”

Section: 4mentioning

confidence: 99%

Can a Single Line of Code Change Society? The Systemic Risks of Optimizing Engagement in Recommender Systems on Global Information Flow, Opinion Dynamics and Social Structures

Chavalarias,

Bouchaud,

Panahi

2024

JASSS

View full text Add to dashboard Cite

As the last few years have seen an increase in both online hostility and polarization, we need to move beyond the fact-checking reflex or the praise for better moderation on social networking sites (SNS) and investigate their impact on social structures and social cohesion. In particular, the role of recommender systems deployed by Very Large Online Platforms (VLOPs) such as Facebook or Twitter has been overlooked. This paper draws on the literature on cognitive science, digital media, and opinion dynamics to propose a faithful replica of the entanglement between recommender systems, opinion dynamics and users' cognitive bias on SNSs like Twitter that is calibrated over a large scale longitudinal database of tweets from political activists. This model makes it possible to compare the consequences of various recommendation algorithms on the social fabric, to quantify their interaction with some major cognitive bias. In particular, we demonstrate that the recommender systems that seek to solely maximize users' engagement necessarily lead to a polarization of the opinion landscape, to a concentration of social power in the hands of the most toxic users and to an overexposure of users to negative content (up to 300% for some of them), a phenomenon called algorithmic negativity bias. Toxic users are more than twice as numerous in the top 1% of the most influential users than in the overall population. Overall, our results highlight the systemic risks generated by certain implementations of recommender systems and the urgent need to comprehensively identify implementations of recommender systems harmful to individuals and society. This is a necessary step in setting up future regulations for systemic SNSs, such as the European Digital Services Act.

show abstract

“…In the early days of Twitter, imbalanced representation and population biases were a major concern for modelling and forecasting [10,11]. The increase of internet users worldwide [12,13] with hints of increasing homogenization among Twitter users [14,15] highlights the potential of social media as a powerful tool for political and societal analysis [16,17].…”

Section: Introductionmentioning

confidence: 99%

Design and analysis of tweet-based election models for the 2021 Mexican legislative election

et al. 2023

View full text Add to dashboard Cite

Modelling and forecasting real-life human behaviour using online social media is an active endeavour of interest in politics, government, academia, and industry. Since its creation in 2006, Twitter has been proposed as a potential laboratory that could be used to gauge and predict social behaviour. During the last decade, the user base of Twitter has been growing and becoming more representative of the general population. Here we analyse this user base in the context of the 2021 Mexican Legislative Election. To do so, we use a dataset of 15 million election-related tweets in the six months preceding election day. We explore different election models that assign political preference to either the ruling parties or the opposition. We find that models using data with geographical attributes determine the results of the election with better precision and accuracy than conventional polling methods. These results demonstrate that analysis of public online data can outperform conventional polling methods, and that political analysis and general forecasting would likely benefit from incorporating such data in the immediate future. Moreover, the same Twitter dataset with geographical attributes is positively correlated with results from official census data on population and internet usage in Mexico. These findings suggest that we have reached a period in time when online activity, appropriately curated, can provide an accurate representation of offline behaviour.

show abstract

Evolution of the political opinion landscape during electoral periods

Cited by 8 publications

References 22 publications

Understanding who talks about what: comparison between the information treatment in traditional media and online discussions

Understanding who talks about what: comparison between the information treatment in traditional media and online discussions

Can a Single Line of Code Change Society? The Systemic Risks of Optimizing Engagement in Recommender Systems on Global Information Flow, Opinion Dynamics and Social Structures

Design and analysis of tweet-based election models for the 2021 Mexican legislative election

Contact Info

Product

Resources

About