Evidence map›Paper›PMID 35966193›Full record

ArticleSocial network analysis and mining2022

Lifetime of tweets: a statistical analysis.

Yashasvi Koul, Kanishk Mamgain, Ankit Gupta

Abstract read
In one paragraph

Article in Social network analysis and mining, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

3 authors.

Yashasvi KoulDepartment of Computer Science and Engineering, Chandigarh College of Engineering and Technology, 160019 Chandigarh, India.
Kanishk MamgainDepartment of Computer Science and Engineering, Chandigarh College of Engineering and Technology, 160019 Chandigarh, India.
Ankit GuptaDepartment of Computer Science and Engineering, Chandigarh College of Engineering and Technology, 160019 Chandigarh, India.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Social media has become such a large part of people's life that even if little at a time, that influence can accommodate over time and can manipulate or even form new opinions. The authors have gathered data with which it is easily understood that the growth of Twitter, the people within its engagement range and its potential for becoming a portal of information sourcing as well as incidents have grown considerably well over the last decade and are well expected to grow into the next decade as well due to the new generation telecom technologies. This study aims to understand how much time Twitter trends remain 'hot' based on various parameters including but not limited to demography, the incident, time period or the people affected.The main objective is to gather data about different trending topics over different time periods and then analyze the pattern of how tweet volume due to that Twitter trend increased or decreased over a few days. This allows to demonstrate that Twitter can be a powerful tool to manipulate public opinion since this reaches a large number of users in a lot of developed countries. The influence of tweets can be seen from the fact that even a tweet done from a non-influential person's account can garner enough attention to become worldwide phenomenon. Towards the end of the study, the authors used a visual medium to depict how various topics fared over the 5 days that tweets were scraped.

Indexed as

DistributionGraphsOpinionsPatterns of trendsTrendTwitter

Identifiers

PMID35966193
PMCPMC9362131

What OpenQuestion holds

Textmetadata
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.