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New research sheds light on 13 ways to gain followers on Twitter
We’ve known for some time that there is a great disparity in the number of followers people have on Twitter – 80% of Twitter users have less than 10 followers, yet some have thousands. A new paper, is the first longitudinal study attempting to correlate features of Twitter users with growth in followers.
The study looked at over half a million tweets from 507 Twitter users and recorded 22 variables which I'll list in order of descending importance according to the results of the study, don't forget this is a correlation study so there will still likely be plenty of other factors at work.
First - How To Lose Followers:
1. Broadcast (only): A lack of interaction in the form of '@replies' and '@mentions' has a profoundly negative effect on gaining new followers. It looks like people may want evidence that if they need to tweet you, you'll actually respond. Interestingly however, interaction did not actually correlate particularly highly with gaining followers - suggesting that there can be too much of a good thing.
2. Be negative: The second best thing you can do if your goal is to lose all of your followers as quickly as you can seems to express negative sentiment - as recorded using a sentiment analysis algorithm. So at this point it is not clear if being the bearer of bad news will cost you followers or if the negative sentiment was in the format of people grumbling about their day - I have a hunch it may prove to be the latter, if this was controlled for it would certainly be an ingenious way of looking at the age old question of whether people tend to share good or bad news.
3. Use of hashtags: The third worst thing you can do is use hashtags excessively. My best guess is that this is because it isn't that common that a hashtag is actually necessary and appropriate - hashtags serve the specific function of drawing together discussion on one topic and it is incredibly #annoying when #hashtags are used #randomly - see what I mean. I'd imagine appropriate use of hashtags won't send your followers packing (or come up as more of a blip on the radar when compared to all tweets) but if you're hashtag use is so high that tweets with hashtags make up a ridiculously high proportion of your tweets then that could be an indicator that you're using hashtags inappropriately - but that's just my hunch.
4. Me, me, me: People whose tweets included a high ratio of self-referential pronouns such as 'me', 'I', 'my', 'we' and 'us' experienced a marginal drop in followers.
How to gain followers:
1. Build a network: The foremost feature of individuals with the most followers was that they had a higher rate of overlapping connections within their contacts.
2. Write tweets that get retweeted: This one is pretty obvious - the users that gained the most followers were also getting more retweets - duh. Though this could be largely circular - a big factor was probably that the same people we consider worth following are also the same people that get retweeted - I guess that could be said for many of these correlations.
3. Spread information and share links: In this study "informational content" was described as tweets containing a URL, RT (retweet), MT (modified tweet), HT (heard through) and tweets containing "via" - all indicators that information is being shared. This one is certainly top of my personal list - I use Twitter as a fountain of information so if someone is clogging up my feed with tweets devoid of information they'll be culled pretty quickly. But it's clearly not just me - the positive effect of informational content was thirty times the (negative) effect of tweets people wrote specifically about themselves.
4. Have a detailed profile: Users with a longer profile description gained more followers.
5. List a URL: Users with a link to a website ended up gaining more followers.
6. Go on tweetingsprees: Users with a high level of 'burstiness' gained more followers, I tend to do this when I can't fit what I need to say in to one tweet or when I'm having a conversation. I read this as implying that interesting people can't always fit everything they want to say into 140 characters and interesting people get followed more, again - just a hunch.
7. High follower to following ratio: People who gained the most followers were followed by more people than they were following. Like point two this is doubtless largely an artefact of underlying factors such as popularity outside of twitter - but at the same time this ratio could be a factor people consciously or subconsciously consider when deciding whether to follow you.
8. Be positive: Users who used positive language gained more followers.
9. Be eloquent: People who used longer (real) words gained more followers.
10: Follow back: Users who followed people who followed them ended up with more followers.
11. Give up your location: People who gained the most followers listed their location in their profile.
12. Engage: A marginal effect was found for a high proportion of favoriting other people's tweets, @replies and @mentions.
13. Stick to a topic: A tiny effect was found for people whose tweets had a high level of the same words coming up again and again.
So all in all, if you want to build followers your best strategy is tweet eloquently about interesting things rather than telling the world what you are putting on your crumpets.
Image Credit: Shutterstock.com / Turtleteeth
The father of all giant sea bugs was recently discovered off the coast of Java.
- A new species of isopod with a resemblance to a certain Sith lord was just discovered.
- It is the first known giant isopod from the Indian Ocean.
- The finding extends the list of giant isopods even further.
Humanity knows surprisingly little about the ocean depths. An often-repeated bit of evidence for this is the fact that humanity has done a better job mapping the surface of Mars than the bottom of the sea. The creatures we find lurking in the watery abyss often surprise even the most dedicated researchers with their unique features and bizarre behavior.
A recent expedition off the coast of Java discovered a new isopod species remarkable for its size and resemblance to Darth Vader.
The ocean depths are home to many creatures that some consider to be unnatural.
According to LiveScience, the Bathynomus genus is sometimes referred to as "Darth Vader of the Seas" because the crustaceans are shaped like the character's menacing helmet. Deemed Bathynomus raksasa ("raksasa" meaning "giant" in Indonesian), this cockroach-like creature can grow to over 30 cm (12 inches). It is one of several known species of giant ocean-going isopod. Like the other members of its order, it has compound eyes, seven body segments, two pairs of antennae, and four sets of jaws.
The incredible size of this species is likely a result of deep-sea gigantism. This is the tendency for creatures that inhabit deeper parts of the ocean to be much larger than closely related species that live in shallower waters. B. raksasa appears to make its home between 950 and 1,260 meters (3,117 and 4,134 ft) below sea level.
Perhaps fittingly for a creature so creepy looking, that is the lower sections of what is commonly called The Twilight Zone, named for the lack of light available at such depths.
It isn't the only giant isopod, far from it. Other species of ocean-going isopod can get up to 50 cm long (20 inches) and also look like they came out of a nightmare. These are the unusual ones, though. Most of the time, isopods stay at much more reasonable sizes.
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During an expedition, there are some animals which you find unexpectedly, while there are others that you hope to find. One of the animal that we hoped to find was a deep sea cockroach affectionately known as Darth Vader Isopod. The staff on our expedition team could not contain their excitement when they finally saw one, holding it triumphantly in the air! #SJADES2018
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What benefit does this find have for science? And is it as evil as it looks?
The discovery of a new species is always a cause for celebration in zoology. That this is the discovery of an animal that inhabits the deeps of the sea, one of the least explored areas humans can get to, is the icing on the cake.
Helen Wong of the National University of Singapore, who co-authored the species' description, explained the importance of the discovery:
"The identification of this new species is an indication of just how little we know about the oceans. There is certainly more for us to explore in terms of biodiversity in the deep sea of our region."
The animal's visual similarity to Darth Vader is a result of its compound eyes and the curious shape of its head. However, given the location of its discovery, the bottom of the remote seas, it may be associated with all manner of horrifically evil Elder Things and Great Old Ones.
If computers can beat us at chess, maybe they could beat us at math, too.
- Most everyone fears that they will be replaced by robots or AI someday.
- A field like mathematics, which is governed solely by rules that computers thrive on, seems to be ripe for a robot revolution.
- AI may not replace mathematicians but will instead help us ask better questions.
The following is an excerpt adapted from the book Shape. It is reprinted with permission of the author.
Will machines replace us? Since the origin of artificial intelligence (AI), people have worried that computers eventually (or even imminently!) will surpass the human cognitive capacity in every respect.
Artificial intelligence pioneer Oliver Selfridge, in a television interview from the early 1960s, said, "I am convinced that machines can and will think in our lifetime" — though with the proviso, "I don't think my daughter will ever marry a computer." (Apparently, there is no technical advance so abstract that people can't feel sexual anxiety about it.)
Let's make the relevant question more personal: will machines replace me? I'm a mathematician; my profession is often seen from the outside as a very complicated but ultimately purely mechanical game played with fixed rules, like checkers, chess, or Go. These are activities in which machines have already demonstrated superhuman ability.
Some people imagine a world where computers give us all the answers. I dream bigger. I want them to ask good questions.
But for me, math is different: it is a creative pursuit that calls on our intuition as much as our ability to compute. (To be fair, chess players probably feel the same way.) Henri Poincaré, the mathematician who re-envisioned the whole subject of geometry at the beginning of the 20th century, insisted it would be hopeless
"to attempt to replace the mathematician's free initiative by a mechanical process of any kind. In order to obtain a result having any real value, it is not enough to grind out calculations, or to have a machine for putting things in order: it is not order only, but unexpected order, that has a value. A machine can take hold of the bare fact, but the soul of the fact will always escape it."
But machines can make deep changes in mathematical practice without shouldering humans aside. Peter Scholze, winner of a 2018 Fields Medal (sometimes called the "Nobel Prize of math") is deeply involved in an ambitious program at the frontiers of algebra and geometry called "condensed mathematics" — and no, there is no chance that I'm going to try to explain what that is in this space.
Meet AI, your new research assistant
What I am going to tell you is the result of what Scholze called the "Liquid Tensor Experiment." A community called Lean, started by Leonardo de Moura of Microsoft Research and now open-source and worldwide, has the ambitious goal of developing a computer language with the expressive capacity to capture the entirety of contemporary mathematics. A proposed proof of a new theorem, formalized by translation into this language, could be checked for correctness automatically, rather than staking its reputation on fallible human referees.
Scholze asked last December whether the ideas of condensed mathematics could be formalized in this way. He also wanted to know whether it could express the ideas of a particularly knotty proof that was crucial to the project — a proof that he was pretty sure was right.
When I first heard about Lean, I thought it would probably work well for some easy problems and theorems. I underestimated it. So did Scholze. In a May 2021 blog post, he writes, "[T]he Experiment has verified the entire part of the argument that I was unsure about. I find it absolutely insane that interactive proof assistants are now at the level that within a very reasonable time span they can formally verify difficult original research."
And the contribution of the machine wasn't just to certify that Scholze was right to think his proof was sound; he reports that the work of putting the proof in a form that a machine could read improved his own human understanding of the argument!
The Liquid Tensor Experiment points to a future where machines, rather than replacing human mathematicians, become our indispensable partners. Whether or not they can take hold of the soul of the fact, they can extend our grasp as we reach for the soul.
Slicing up a knotty problem
That can take the form of "proof assistance," as it did for Scholze, or it can go deeper. In 2018, Lisa Piccirillo, then a PhD student at the University of Texas, solved a long-standing geometry problem about a shape called the Conway knot. She proved the knot was "non-slice" — this is a fact about what the knot looks like from the perspective of four-dimensional beings. (Did you get that? Probably not, but it doesn't matter.) The point is this was a famously difficult problem.
A few years before Piccirillo's breakthrough, a topologist named Mark Hughes at Brigham Young had tried to get a neural network to make good guesses about which knots were slice. He gave it a long list of knots where the answer was known, just as an image-processing neural net would be given a long list of pictures of cats and pictures of non-cats.
Hughes's neural net learned to assign a number to every knot; if the knot were slice, the number was supposed to be 0, while if the knot were non-slice, the net was supposed to return a whole number bigger than 0. In fact, the neural net predicted a value very close to 1 — that is, it predicted the knot was non-slice — for every one of the knots Hughes tested, except for one. That was the Conway knot.
For the Conway knot, Hughes's neural net returned a number very close to 1/2, its way of saying that it was deeply unsure whether to answer 0 or 1. This is fascinating! The neural net correctly identified the knot that posed a really hard and mathematically rich problem (in this case, reproducing an intuition that topologists already had).
Some people imagine a world where computers give us all the answers. I dream bigger. I want them to ask good questions.
Dr. Jordan Ellenberg is a professor of mathematics at the University of Wisconsin and a number theorist whose popular articles about mathematics have appeared in the New York Times, the Wall Street Journal, Wired, and Slate. His most recent book is Shape: The Hidden Geometry of Information, Biology, Strategy, Democracy, and Everything Else.
Laughing gas may be far more effective for some than antidepressants.
- Standard antidepressant medications don't work for many people who need them.
- With ketamine showing potential as an antidepressant, researchers investigate another anesthetic: nitrous oxide, commonly called "laughing gas."
- Researchers observe that just a light mixture of nitrous oxide for an hour alleviates depression symptoms for two weeks.
The usual antidepressants don't work for everyone. That's what makes a new study of the antidepressant properties of nitrous oxide so intriguing. It looks like just a single low dose of what your dentist may call "laughing gas" can help alleviate symptoms of depression for weeks afterward.
The study, from researchers at University of Chicago and Washington University-St. Louis, is published in the journal Science Translational Medicine.
Resistance to anti-depression medications
Nitrous oxide: two atoms of nitrogen, one of oxygenCredit: Big Think
According to the senior author of the study, Charles Conway, "A significant percentage — we think around 15 percent — of people who suffer from depression don't respond to standard antidepressant treatment."
"These 'treatment-resistant depression' patients," Conway says, "often suffer for years, even decades, with life-debilitating depression. We don't really know why standard treatments don't work for them, though we suspect that they may have different brain network disruptions than non-resistant depressed patients. Identifying novel treatments, such as nitrous oxide, that target alternative pathways is critical to treating these individuals."
"There is a huge unmet need," says lead author Peter Nagele. "There are millions of depressed patients who don't have good treatment options, especially those who are dealing with suicidality."
If ketamine can help, can nitrous oxide?
Credit: sudok1 / Adobe Stock
The researchers wondered if some of the anti-depression properties seen in ketamine might also apply to nitrous oxide. Nagele explains, "Like nitrous oxide, ketamine is an anesthetic, and there has been promising work using ketamine at a sub-anesthetic dose for treating depression."
The researchers conducted a one-hour session — they describe it as a "proof-of-principle" trial — in which 20 individuals with depression were administered an air mixture with 50 percent nitrous oxide. Twenty-four hours later, the researchers found a significant reduction in the participants' symptoms of depression versus a control group.
However, the individuals also suffered the unpleasant side effects that laughing gas often causes in dental patients: headache, nausea, and vomiting.
Smaller dose, longer effect
Credit: sudok1 / Adobe Stock
"We wondered if our past concentration of 50 percent had been too high," recalls Nagele. "Maybe by lowering the dose, we could find the 'Goldilocks spot' that would maximize clinical benefit and minimize negative side effects."
In a new trial, 20 people with depression were given a lighter nitrous oxide mix, just 25 percent, and the individuals tested reported a 75 percent reduction in side effects compared to the a control group given an air/oxygen placebo. This time, the researchers also tracked the effect of nitrous oxide on symptoms of depression for a far longer period, two weeks instead of just 24 hours.
"The reduction in side effects was unexpected and quite drastic," reports Nagele, "but even more excitingly, the effects after a single administration lasted for a whole two weeks. This has never been shown before. It's a very cool finding."
Nagele also notes that, despite its popular renown as laughing gas, even a light 25 percent mix of nitrous actually causes people to nod off. "They're not getting high or euphoric; they get sedated."
Delivering help to people with depression
Nagele cautions, "These have just been pilot studies. But we need acceptance by the larger medical community for this to become a treatment that's actually available to patients in the real world. Most psychiatrists are not familiar with nitrous oxide or how to administer it, so we'll have to show the community how to deliver this treatment safely and effectively. I think there will be a lot of interest in getting this into clinical practice."
After all, Nagele adds, "If we develop effective, rapid treatments that can really help someone navigate their suicidal thinking and come out on the other side — that's a very gratifying line of research."