First- and second-level agenda setting in the Twittersphere: An application to the Italian political debate

Cerón, Andrea; Curini, Luigi; Iacus, Stefano Maria

doi:10.1080/19331681.2016.1160266

Cited by 67 publications

(51 citation statements)

References 62 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Sentiment analysis has become a major area of interest in communication research. Recent applications include analyses of media tone (Van Atteveldt, Kleinnijenhuis et al, 2008a;Hopkins & King, 2010;Soroka & McAdams, 2015;Soroka, Young, & Balmas, 2015;Young & Soroka, 2012), agenda setting (Ceron, Curini, & Iacus, 2016), framing (Burscher, Odijk, Vliegenthart, De Rijke, & De Vreese, 2014), election forecasting (Ceron, Curini, & Iacus, 2015, and candidate evaluations (Aaldering & Vliegenthart, 2016). These studies do automated text analysis with sentiment dictionaries (Aaldering & Vliegenthart, 2016;Young & Soroka, 2012) or use machine learning (Van Atteveldt, Kleinnijenhuis et al, 2008a;Burscher et al, 2014;Ceron et al, 2016;Hopkins & King, 2010) to get sentiment scores.…”

Section: Introductionmentioning

confidence: 99%

More than Bags of Words: Sentiment Analysis with Word Embeddings

Rudkowsky

Haselmayer

Wastian

et al. 2018

Communication Methods and Measures

169

102

View full text Add to dashboard Cite

Moving beyond the dominant bag-of-words approach to sentiment analysis we introduce an alternative procedure based on distributed word embeddings. The strength of word embeddings is the ability to capture similarities in word meaning. We use word embeddings as part of a supervised machine learning procedure which estimates levels of negativity in parliamentary speeches. The procedure's accuracy is evaluated with crowdcoded training sentences; its external validity through a study of patterns of negativity in Austrian parliamentary speeches. The results show the potential of the word embeddings approach for sentiment analysis in the social sciences.

show abstract

Section: Introductionmentioning

confidence: 99%

More than Bags of Words: Sentiment Analysis with Word Embeddings

Rudkowsky

Haselmayer

Wastian

et al. 2018

Communication Methods and Measures

169

102

View full text Add to dashboard Cite

show abstract

“…A similar model is studied in Robert & Rosenbaum [39] with a different methodology. Empirical applications of Hoffmann et al [27]'s methodology are found in [2,6,8,28]. Apart from Brownian motion driven models, the Hawkes processes may be a credible candidate to describe lead-lag effects in the continuous-time framework; see Bacry et al [3] and Da Fonseca & Zaatour [10] for example.…”

Section: Introductionmentioning

confidence: 99%

Wavelet-Based Methods for High-Frequency Lead-Lag Analysis

Hayashi¹,

Koike

2018

SIAM J. Finan. Math.

View full text Add to dashboard Cite

We propose a novel framework to investigate lead-lag relationships between two financial assets. Our framework bridges a gap between continuous-time modeling based on Brownian motion and the existing wavelet methods for lead-lag analysis based on discrete-time models and enables us to analyze the multi-scale structure of lead-lag effects. We also present a statistical methodology for the scale-by-scale analysis of lead-lag effects in the proposed framework and develop an asymptotic theory applicable to a situation including stochastic volatilities and irregular sampling. Finally, we report several numerical experiments to demonstrate how our framework works in practice. J for some universal constant c 4 > 0, hence we obtainWe rewrite the target quantity as, we obtain (35) by the dominated convergence theorem once we prove Ξ J (i 1 , i 2 ) → 0 for any fixed i 1 , i 2 . By Lemma 7 we haveWe can take sufficiently large r such that 2/r < 1 − κ. Then we haveJ )|X]| r ] = O τ 1−2/r J L 2 log 2 τ J r/2 = o(1) by Lemma 12. This yields the desired result. N τ J by the triangle and Schwarz inequalities. Therefore, the Markov inequality yields P max l∈L + J |I J (l)| > ε ≤ ε −1 l∈L + J I J (l) 2 2 N 2 τ J for any ε > 0, hence max l∈L + J |I J (l)| → p 0.Noting that L 2 τ J → 0, we can prove max l∈L + J |II J (l)| → p 0 in an analogous manner to the above.Next we prove max l∈L + J |III J (l)| → p 0. For any k ∈ I m,N (i) we haveWe can prove max l∈L + J |IV J (l)| → p 0 in an analogous manner.Now we prove max l∈L + J |V J (l)| → p 0. By Lemma 14 and the boundedness of σ 1 and σ 2 , we haveψ 1 → 0 by assumptions. This especially implies that max l∈L + J |V J (l)| → p 0. Finally, by Lemmas 4(b) and 8 we have max l∈L + J |VI J (l)| = o p (M J · τ −1 J τ m · τ J ). Since M J = O(τ −1 m ), we obtain max l∈L + J |VI J (l)| → p 0. This completes the proof. Completion of the proof of Theorem 2We need the following auxiliary result:Lemma 15. Λ −j D(λ)Π(λ)e

show abstract

“…The use of framing in studying the contents of media coverage is called the second level agenda-setting theory. For instance, a study on the perceptions of Mexican voters settling in the United State toward newspaper coverage concerning Mexico during United State presidential election in 1996 [13]; a study reporting forest pollution in Canada and Sweden [14]; a study on Twitter during political debate in Italy [15].…”

Section: B Level Agenda Settingmentioning

confidence: 99%

The Agenda Formation of the Fantasy of Identity Politics in the Digital Media and its Effect towards Cohesiveness of 212 Alumni Brotherhood

Sulistyanto¹,

Sovianti²,

Syaifuddin³

2019

Proceedings of the First International Conference on Administration Science (ICAS 2019)

View full text Add to dashboard Cite

The research aims to reveal the agenda formation of the fantasy of identity politics compounded in the narration of radical Islamic media digital agenda and its effect toward cohesiveness of 212 Alumni brotherhood (PA 212). The research uses mixed methods. The first step is conducting content analysis, framing analysis, and theme fantasy analysis to reveal the media agenda on identity politics. The digital media that is analyzed amounts to 13 media. The second step is performing surveys applying simple random sampling with 86 respondents. The research finds that there is a positive correlation between the agenda formation of the fantasy of identity politics in the digital media and cohesiveness formation of PA 212. Fantasy construction in the digital media produces strong cohesiveness to public agenda so that it is capable of breeding social movement. Theoretically, this research is a new development in the agendasetting theory by introducing the existence of the fantasy construction on media agenda and public agenda. Practically, this research suggests the government release a regulation or laws about the establishment of digital media, so that the dissemination of hoax can be suppressed and monitored. Hopefully, the idea can also be a preventive manner to avoid political movements that are going to change the constitution with other various reasons and hidden interests.

show abstract

First- and second-level agenda setting in the Twittersphere: An application to the Italian political debate

Cited by 67 publications

References 62 publications

More than Bags of Words: Sentiment Analysis with Word Embeddings

More than Bags of Words: Sentiment Analysis with Word Embeddings

Wavelet-Based Methods for High-Frequency Lead-Lag Analysis

The Agenda Formation of the Fantasy of Identity Politics in the Digital Media and its Effect towards Cohesiveness of 212 Alumni Brotherhood

Contact Info

Product

Resources

About