Evidence map›Paper›PMID 27835683›Full record

ArticlePloS one2016

Cohesion and Coalition Formation in the European Parliament: Roll-Call Votes and Twitter Activities.

Darko Cherepnalkoski, Andreas Karpf, Igor Mozetič, Miha Grčar

Abstract read
In one paragraph

Article in PloS one, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

11 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Darko CherepnalkoskiDepartment of Knowledge Technologies, Jožef Stefan Institute, Ljubljana, Slovenia.
Andreas KarpfUniversité Panthéon-Sorbonne / Paris School of Economics, Paris, France.
Igor MozetičDepartment of Knowledge Technologies, Jožef Stefan Institute, Ljubljana, Slovenia.
Miha GrčarDepartment of Knowledge Technologies, Jožef Stefan Institute, Ljubljana, Slovenia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

We study the cohesion within and the coalitions between political groups in the Eighth European Parliament (2014-2019) by analyzing two entirely different aspects of the behavior of the Members of the European Parliament (MEPs) in the policy-making processes. On one hand, we analyze their co-voting patterns and, on the other, their retweeting behavior. We make use of two diverse datasets in the analysis. The first one is the roll-call vote dataset, where cohesion is regarded as the tendency to co-vote within a group, and a coalition is formed when the members of several groups exhibit a high degree of co-voting agreement on a subject. The second dataset comes from Twitter; it captures the retweeting (i.e., endorsing) behavior of the MEPs and implies cohesion (retweets within the same group) and coalitions (retweets between groups) from a completely different perspective. We employ two different methodologies to analyze the cohesion and coalitions. The first one is based on Krippendorff's Alpha reliability, used to measure the agreement between raters in data-analysis scenarios, and the second one is based on Exponential Random Graph Models, often used in social-network analysis. We give general insights into the cohesion of political groups in the European Parliament, explore whether coalitions are formed in the same way for different policy areas, and examine to what degree the retweeting behavior of MEPs corresponds to their co-voting patterns. A novel and interesting aspect of our work is the relationship between the co-voting and retweeting patterns.

Indexed as

PoliticsCooperative BehaviorDatasets as TopicEuropean UnionHumansPolicy MakingPolitical SystemsSocial Media

Identifiers

PMID27835683
PMCPMC5106041

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.