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#iconohash presents #phdchat

Scroll down for the #phdchat conversation report for November 5th, 2020 EST.

iconohash

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#phdchat for November 5th, 2020 EST

01
3rd

coming together

When it comes to participants, we ranked 3rd among 204 conversations.

3rd

participating

In terms of the number of posts, we ranked 3rd out of 204 conversations that took place.

1st

amplification

This conversation ranked 1st out of 204 in terms of the amplification of ideas & content.

how we participated. Here's how participants reacted & engaged each other

Reactions
82%

Amplified

There were 369 reshares, 82% of posts, during the course of the conversation.

0%

Engaged

0% of participants in this conversation engaged each other 1 times.

6%

Content

6% of participants in this conversation shared a total of 3 unique pieces of content.

quick facts.

Here are the basic facts of the conversation for for November 5th, 2020.

0
Active
Participants
0
Posts
Total

meet the influencers the movers and shakers of #phdchat

02

the top of the list. Top influencers in the chat

IMPACT
@DrLindelaniM

@DrLindelaniM

Lindelani Mnguni, PhD.
I think.

@OpenAcademics

@OpenAcademics

OpenAcademics
Twitter community to support fellow academics across all disciplines. @OpenAcademics for RT. #DiversityandInclusion #MentalHealth #OAposter

@ProofItEditing

@ProofItEditing

Rachael Lammie
Freelance Editor

about the participants. let's take a look at everyone who show'd up

03

top professions what are they doing when they're not in #phdchat

insight

  the top participating professions
out of 58 total

research
writer
professor
biologist
academic

gender an estimation of what the gender break down looks like
= 1 men or women

insight

Women

Men

about the content. the best content that was shared

04
Spatial clustering and modelling for landslide susceptibility mapping in the north of the Kathmandu Valley, Nepal | SpringerLink
In this article, we propose and test alternative sampling strategies based on clustering distribution concepts to increase the efficiency of the landslide susceptibility model outcomes, instead of common random selection method for training and testing samples. To that end, we prepared a comprehensive landslide inventory and used six unsupervised clustering algorithms (K-means, K-medoids, hierarchical cluster (HC) analysis, expectation–maximization using Gaussian mixture models (EM/GMM), affinit

chat growth how is #phdchat growing over time?

05

want more? are you interested in the full data analysis for this chat? Full transcript? Longer view at chat growth?

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