Twitter loses nearly half advertising revenue since Elon Musk takeover

Surprise?

Twitter loses nearly half advertising revenue since Elon Musk takeover  By Jemma Dempsey   BBC News

Twitter has lost almost half of its advertising revenue since it was bought by Elon Musk for $44bn (£33.6bn) last October, its owner has revealed.

He said the company had not seen the increase in sales that had been expected in June, but added that July was a "bit more promising".

Mr Musk sacked about half of Twitter's 7,500 staff when he took over in 2022 in an effort to cut costs.

Rival app Threads now has 150 million users, according to some estimates.

Its in-built connection to Instagram automatically gives the Meta-designed platform access to a potential two billion users.

Meanwhile, Twitter is struggling under a heavy debt load. Cash flow remains negative, Mr Musk said at the weekend, although the billionaire did not put a time frame on the 50% drop in ad revenue. ... ' 

10 million sign up for Meta's Twitter rival app, Threads

Media to extend back.

10 million sign up for Meta's Twitter rival app, Threads

By NPR, July 6, 2023

The logo for Meta's new app, Threads.

Meta's new app appears to mimic Twitter, a direct challenge to the social media platform owned by Elon Musk.

Credit: Richard Drew/AP

Meta has unveiled an app called Threads to rival Twitter, targeting users looking for an alternative to the social media platform owned — and frequently changed — by Elon Musk.

Threads is billed as a text-based version of Meta's photo-sharing app Instagram that the company says provides "a new, separate space for real-time updates and public conversations."

It went live late Wednesday in Apple and Google Android app stores, with CEO Mark Zuckerberg saying 10 million people had signed up in the first seven hours. There were some early glitches, including Zuckerberg's posts — or Threads as they're dubbed — not loading in several places including the United Kingdom, India and Lebanon. But his replies to other users did appear.

Threads launched in more than 100 countries — including the U.S., Britain, Australia, Canada and Japan — and has already drawn celebrity users like chef Gordon Ramsay, pop star Shakira and actor Jack Black as well as accounts from Airbnb, Guinness World Records, Netflix, Vogue magazine and other media outlets.

From NPR

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Musk Takes Twitter

Worth a look to think of implications, who guarantees freedom of speech? 

A complete timeline of the Elon Musk-Twitter saga   from TechCrunch

It’s been a wild month of news for the social network that we collectively love to hate. In early April, Elon Musk took a bite out of Twitter, coming away with 9.2% of the company and plans to exercise his influence over the company through its board. After that he backed out of his planned board seat, Musk teed up an even more outrageous plan: He’d buy the company outright and take it private.  ....  '

Elon Musk says he wants to improve Twitter by "making the algorithms open source to increase trust, defeating the spam bots, and authenticating all humans" (The Verge)

Predicting What you Will Say

A kind of prediction classification assumption.      What exactly you will say, or rather how what you will say is readily classifiable as well?  Implications in marketing and advertising?    Speaking/writing is only part of your behavior.

Social media can predict what you’ll say, even if you don’t participate

On twitter, your words are predictable using the words of your network.  By John Timmer in Ars Technica

There have been a number of high-profile criminal cases that were solved using the DNA that family members of the accused placed in public databases. One lesson there is that our privacy isn't entirely under our control; by sharing DNA with you, your family has the ability to choose what everybody else knows about you.

Now, some researchers have demonstrated that something similar is true about our words. Using a database of past tweets, they were able to effectively pick out the next words a user was likely to use. But they were able to do so more effectively if they simply had access to what a person's contacts were saying on Twitter. .... "

Tweets Predict Location

More indications that simple online messages can betray location.

Your Old Tweets Give Away More Location Data Than You Think    In Wired 

An international team of researchers has developed an algorithm that uses Twitter to automatically predict a user's location within minutes.

The Location Privacy Auditor (LPAuditor) exploits the inclusion of global-positioning system (GPS) coordinates within geotagged tweets as part of each tweet's metadata, accessible via Twitter's application programming interface (API).

Sharing location data is now an opt-in policy rather than automatic, but the GPS data users shared before the update is still available through the API.

LPAuditor is designed to analyze geotagged tweets and deduce detailed information about people's most sensitive locations.   Its analysis of coordinate clusters and timestamps on tweets allowed it to infer thousands of users' whereabouts. .... " 

Bots in the Twittersphere

A quite interesting view of Bots in the twittersphere.  From PEW below.  Useful definitions.   Which points out that they are not close to mostly out to make mischief, but also a useful part of the infrastructure.   Call them automated accounts if you like.  Just like there are many, many automated systems out there,  which query their environment and some action:  assisting, adapting, counting, sensing .....  You could call it all an IOB  (internet of Bots)  Some human,  some machine, some sensor ...  simple and complex.  Makes me think about about the detection of intent.   Can we measure the intent of an automated bot?    By its actions, developers?

A considerable piece of work below that made methink ... read it all:

Bots in the Twittersphere
An estimated two-thirds of tweeted links to popular websites are posted by automated accounts – not human beings .... " 

      ... By Stefan Wojcik, Solomon, Messing  AAron Smith, Lee Arainie and Paul Hitlin

Library of Congress Stops Archiving all Tweets

The LOC started archiving tweets in 2006, but has now announced it will do so only very selectively,  of historically important material,  starting in the new year.

Twitter Posts

My Twitter stream https://twitter.com/franzd  contains links to many of the posts here.   I also add related posts from twitter and other social sources a similar range of topics.   Follow along there and add your own references.

This Blog on Twitter

From nearly the beginning, this blog has been connected to Twitter.   You can follow it there from my handle @FranzD.  I  selectively mention many posts here.   Some people find that simpler to follow.  See: https://twitter.com/franzd

Marketing Data on Demand

Google announces  Marketing Data on Demand   via Twitter.  In Think with Google.

" ... Get excited, marketing gurus! Today we’re rolling out a brand new (and super cool) data on demand service that will do exactly that—give you the data you need, on demand—using Twitter. 

You’re in the trenches every day, and it’s hard to stay up to speed. We get it. And that’s why we did the heavy lifting for you. From measurement to mobile, autos to AdWords, we’re putting hundreds of data points at your fingertips.  .... " 

It’s super easy, too. Just ask us for data on Twitter and we’ll respond with the data you want ... " 

Associated Twitter Feed

Posts in this blog are selectively mentioned in my twitter feed: @FranzD.  That feed also contains retweets of related material that deals with analytics, data issues, problem solving, AI, process, retail, marketing, advisory systems and other topics mentioned in this blog.

Predicting Demographics with Twitter

Always an interesting challenge.  Note the mention of Mechanical Turk, a method we examined.

In Techrepublic:
"  ... Big data can reveal inaccurate stereotypes on Twitter, according to UPenn study ... What can you guess about a person, based on their tweets? A new study examined how 140 characters can shape assumptions about a person's gender, age, education, and politics. Here's what it found.  ...  " 

By Hope Reese  | November 16, 2016

IBM and Chevy Generating 'Positivity'

Anther example of the text analysis of social media streams  ...  although the resulting measure seems quite general.   In AdAge:

Chevy and IBM Will Rate Your Social Media 'Positivity'     By E.J. Schultz

" ... The global campaign -- which is rolling out today -- allows participants to visit a mobile or desktop site to obtain a positivity score based on their social media posts.

Users are asked to enter either their Twitter or Facebook account names at the site. Within seconds the program returns a positivity score. The output also identifies a user's least positive and most positive posts as well as their five most used positive words. Also provided is a detailed social personality summary. At the end, Chevy recommends an experience based on the output, such as "master a new language."   ... " 

Detecting Review and Follower Fraud

From CMU.  Tested against some very large set of Twitter data.   Uses a form of Graph Analytics.  Python code is available as Open Source.  Uses a form of  Faloutsos' NetProbe  (technical paper) for the analysis.

New algorithm detects online fraudsters: Method sees through camouflage to reveal fake followers, reviewers.

The method, called FRAUDAR, marks the latest escalation in the cat-and-mouse game played by online fraudsters and the social media platforms that try to out them. In particular, the new algorithm makes it possible to see through camouflage that fraudsters use to make themselves look legitimate, said Christos Faloutsos, professor of machine learning and computer science.

In real-world experiments using Twitter data for 41.7 million users and 1.47 billion followers, FRAUDAR fingered more than 4,000 accounts not previously identified as fraudulent, including many that used known follower-buying services such as TweepMe and TweeterGetter.

"We're not identifying anything criminal here, but these sorts of frauds can undermine people's faith in online reviews and behaviors," Faloutsos said. He noted most social media platforms try to flush out such fakery, and FRAUDAR's approach could be useful in keeping up with the latest practices of fraudsters.

The CMU algorithm is available as open-source (Python) code at http://www.andrew.cmu.edu/user/bhooi/camo.zip. A research paper describing the algorithm won the Best Paper Award last month at the Association for Computing Machinery's Conference on Knowledge Discovery and Data Mining (KDD2016) in San Francisco. ..... " 

Power BI Now Does Twitter Analytics

Been looking at the analysis of twitter streams lately, new from MS Power BI.  A campaign management solution template.

Microsoft Power BI Blog
Announcing the brand & campaign management solution template for Twitter  Justyna Lucznik, Program Manager

Today we are excited to announce the release of the brand and campaign management solution template for Twitter. Imagine you are an event manager who has spent the past few months planning a conference, lining up speakers, and coordinating catering, venues, and transportation. The big week is approaching, and you want to make sure everything goes smoothly. You want to understand what is well received and respond to negative sentiment immediately. You want to recognize which topics related to your event are trending and how those change across time. You want to find your biggest influencers, fans and critics.

Now imagine that you can set up sophisticated analytics that could answer all those questions for you in the space of five minutes.

Our new brand and campaign management solution template will help you do just that. Whether you manage an event, product, or marketing campaign, you can use our template to quickly and easily do analytics on top of Twitter data. All you will need to get started are Twitter credentials, the search terms you want to track, and an Azure subscription. (Don’t worry if you don’t have one – we help you get started with a trial as well).  ... "  

Looking for Sarcasm

Have been looking at the general problem of how to detect demographic and personality indicators via twitter streams.  Here is an example described of detecting sarcasm . 

" ... Researchers at the University of Lisbon have developed a machine-learning system that can identify sarcasm on Twitter by examining a user's past tweets. The system uses the past tweets to develop a picture of a person that is detailed enough to guess when they are being sarcastic. The researchers say the system predicts sarcasm with an accuracy of 87 percent, which is slightly better than other existing methods. However, by learning to detect sarcasm without human input, the system should be easy to use. In addition, the new approach should work for any language and any online platform where posting history is available. "The key innovation is realizing you can build a model of the user merely based on what they have said in the past," says University of Lisbon researcher Silvio Amir. Monash University researcher Mark Carmen notes it should be straightforward to integrate the new approach with other types of social media analysis, such as tracking users' emotions or stock market trends. Analyzing sarcasm also could be a great help to marketers and customer service teams, as well as virtual assistants such as Apple's Siri. ...  "

See previous coverage of sarcasm below.

Twitter and other Social Connections

Many of the blog posts here are also on my twitter feed:  @FranzD    that feed also repeats information from other information streams I am connected to in Advanced Tech, Retail, AI, Analytics and Cognitive.   Follow me there.   The blog is also repeated in Flipboard.

Flu Prediction Cognitive System

Using Watson, Twitter and CDC Data for predicting flu epidemics. Modeling under specific contexts. By Prof. Dr. Gordon Pipa, University of Osnabrueck  Embedded in the slide presentation.....    Audio recording.   Reminiscent of some of our own work in bioterror modeling , previously mentioned here.

Re Emergence of AI in the Enterprise

Seeing this directly and also through increasing queries about what to do next to play.  Good overview.

Artificial intelligence in the enterprise: It’s on  
Companies as diverse as Pitney Bowes, General Electric and Twitter are taking the plunge into machine learning. Here’s why.  ,,, 

Social Media Analysis Using Watson Analytics

Saw a presentation today of the use of Watson Analytics using Twitter data.  Using the recent Golden Globes presentation as an example.  This comes from the collaboration between Twitter and IBM .  This demonstrates the use of automated and directed visual analytics and storytelling using Watson Analytics.   Good example.  For now this capability is free as part of Watson analytics.  Presentation Slides here.