Dataset Information

Investigating Italian disinformation spreading on Twitter in the context of 2019 European elections.

ABSTRACT: We investigate the presence (and the influence) of disinformation spreading on online social networks in Italy, in the 5-month period preceding the 2019 European Parliament elections. To this aim we collected a large-scale dataset of tweets associated to thousands of news articles published on Italian disinformation websites. In the observation period, a few outlets accounted for most of the deceptive information circulating on Twitter, which focused on controversial and polarizing topics of debate such as immigration, national safety and (Italian) nationalism. We found evidence of connections between Italian disinformation sources and different disinformation outlets across Europe, U.S. and Russia, featuring similar, even translated, articles in the period before the elections. Overall, the spread of disinformation on Twitter was confined in a limited community, strongly (and explicitly) related to the Italian conservative and far-right political environment, who had a limited impact on online discussions on the up-coming elections.

SUBMITTER: Pierri F

PROVIDER: S-EPMC6968875 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Investigating Italian disinformation spreading on Twitter in the context of 2019 European elections.

Pierri Francesco F Artoni Alessandro A Ceri Stefano S

PloS one 20200117 1

We investigate the presence (and the influence) of disinformation spreading on online social networks in Italy, in the 5-month period preceding the 2019 European Parliament elections. To this aim we collected a large-scale dataset of tweets associated to thousands of news articles published on Italian disinformation websites. In the observation period, a few outlets accounted for most of the deceptive information circulating on Twitter, which focused on controversial and polarizing topics of deb ...[more]

PMID: 31951628

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Project description:BackgroundLow back pain (LBP) remains the leading cause of disability worldwide. A better understanding of the beliefs regarding LBP and impact of LBP on the individual is important in order to improve outcomes. Although personal experiences of LBP have traditionally been explored through qualitative studies, social media allows access to data from a large, heterogonous, and geographically distributed population, which is not possible using traditional qualitative or quantitative methods. As data on social media sites are collected in an unsolicited manner, individuals are more likely to express their views and emotions freely and in an unconstrained manner as compared to traditional data collection methods. Thus, content analysis of social media provides a novel approach to understanding how problems such as LBP are perceived by those who experience it and its impact.ObjectiveThe objective of this study was to identify contextual variables of the LBP experience from a first-person perspective to provide insights into individuals' beliefs and perceptions.MethodsWe analyzed 896,867 cleaned tweets about LBP between January 1, 2014, and December 31, 2018. We tested and compared latent Dirichlet allocation (LDA), Dirichlet multinomial mixture (DMM), GPU-DMM, biterm topic model, and nonnegative matrix factorization for identifying topics associated with tweets. A coherence score was determined to identify the best model. Two domain experts independently performed qualitative content analysis of the topics with the strongest coherence score and grouped them into contextual categories. The experts met and reconciled any differences and developed the final labels.ResultsLDA outperformed all other algorithms, resulting in the highest coherence score. The best model was LDA with 60 topics, with a coherence score of 0.562. The 60 topics were grouped into 19 contextual categories. "Emotion and beliefs" had the largest proportion of total tweets (157,563/896,867, 17.6%), followed by "physical activity" (124,251/896,867, 13.85%) and "daily life" (80,730/896,867, 9%), while "food and drink," "weather," and "not being understood" had the smallest proportions (11,551/896,867, 1.29%; 10,109/896,867, 1.13%; and 9180/896,867, 1.02%, respectively). Of the 11 topics within "emotion and beliefs," 113,562/157,563 (72%) had negative sentiment.ConclusionsThe content analysis of tweets in the area of LBP identified common themes that are consistent with findings from conventional qualitative studies but provide a more granular view of individuals' perspectives related to LBP. This understanding has the potential to assist with developing more effective and personalized models of care to improve outcomes in those with LBP.

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Dataset Information

Investigating Italian disinformation spreading on Twitter in the context of 2019 European elections.

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Investigating Italian disinformation spreading on Twitter in the context of 2019 European elections.

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