Evidence map›Paper›PMID 33265853›Full record

ArticleEntropy (Basel, Switzerland)2018

How News May Affect Markets' Complex Structure: The Case of Cambridge Analytica.

Antonio Peruzzi, Fabiana Zollo, Walter Quattrociocchi, Antonio Scala

Open access · goldAbstract read
In one paragraph

Article in Entropy (Basel, Switzerland), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
4.4field-weighted citation impact, top 5% of its field
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

2 citing papers in PubMed, 16 citations in OpenAlex.

  1. Article
  2. Article
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 at 2 institutions in 2 countries.

Antonio PeruzziThe Department of Environmental Sciences, Informatics and Statistics (DAIS), Ca' Foscari University of Venice, Venezia 30123, Italy.
Fabiana ZolloThe Department of Environmental Sciences, Informatics and Statistics (DAIS), Ca' Foscari University of Venice, Venezia 30123, Italy.ORCID 0000-0002-0833-5388
Walter QuattrociocchiThe Department of Environmental Sciences, Informatics and Statistics (DAIS), Ca' Foscari University of Venice, Venezia 30123, Italy.
Antonio ScalaIstituto dei Sistemi Complessi (ISC)-CNR, UOS Sapienza, Roma 00185, Italy.ORCID 0000-0002-3414-2686
Ca' Foscari University of Venice · ITLondon Institute for Mathematical Sciences · GB

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The claim of Cambridge Analytica, a political consulting firm, that it was possible to influence voting behavior by using data mined from the social platform Facebook created a sudden fear in its users of being manipulated; consequently, even the market price of the social platform was shocked.We propose a case study analyzing the effect of this data scandal not only on Facebook stock price, but also on the whole stock market. To such a scope, we consider 15-minutes prices and returns of the set of the NASDAQ-100 components before and after the Cambridge Analytica case. We analyze correlations and Mutual Information among components finding that assets become more correlated and their Mutual Information grows higher. We also observe that correlation and Mutual Information are mutually increasing and seem to follow a master curve. Hence, the market appears more fragile after the Cambridge Analytica event. In fact, as it is well-known in finance, an increase in the average value of correlations augments the systemic risk (i.e., all the market can collapse as a whole) and decreases the possibility of allocating a safe investment portfolio.

Indexed as

complex networksfinancial seriessocial media

Identifiers

PMID33265853
PMCPMC7512327
OpenAlexW2894572059

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.