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Rise in Twitter Shares by 13 Percent by Targeting Better Ads and Weeding Out Spam, Abusive Posts

For the first quarter, Twitter's revenue rose 18 percent to $787 million from the year-ago quarter, topping analyst estimates of $776.1 million

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FILE - The Twitter application is seen on a phone screen. VOA

Shares in Twitter Inc jumped 13 percent Tuesday after the social media company reported quarterly revenue above analyst estimates, which executives said was the result of weeding out spam and abusive posts and targeting ads better.

New ad formats, partnerships with content providers like the U.S. National Basketball Association and efforts to patrol abusive content are helping Twitter better compete for advertising dollars, executives said.

Social media companies have been under pressure over privacy concerns and political influence activity. Twitter has removed thousands of spam and suspicious accounts, which it blamed for sequential declines in monthly users in recent quarters.

Twitter executives said they saw opportunities for selling ads that earn revenue when users visit websites or download apps, citing success with major brands like Walt Disney Co. The company is looking to grow its sales team in 2019 to better serve big advertisers.

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Twitter reported quarterly profit of $191 million, or 25 cents a share, compared with $61 million, or 8 cents per share, a year earlier. Pixabay

“Something where you see a blending of performance and brand is the Star Trek ad that Disney is running right now, where I click through to make sure that I’d be notified when more information was available about the next Star Wars,” Twitter Chief Financial Officer Ned Segal told analysts.

Twitter said pre-roll ads, or promotional messages that play before videos, are also growing.

The company said its monthly active users (MAU) rose 9 million to 330 million from the previous quarter, much better than Wall Street’s average estimate that it would lose 2.2 million users, according to IBES data from Refinitiv. Still, MAUs were down 6 million from a year earlier. It was Twitter’s last quarter of disclosing MAUs.

From now on it will only provide “monetizable” daily active users (mDAUs), created to measure people exposed to advertising and exclude those who access Twitter via text messages or aggregating sites like TweetDeck.

For the first quarter, Twitter said mDAUs rose to 134 million, up 12 percent from a year ago. Analysts were encouraged by signs the company had found ways to sustainably grow users and revenue, but said the new way of measuring users could make comparisons with rivals like Facebook Inc more difficult.

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New ad formats, partnerships with content providers like the U.S. National Basketball Association and efforts to patrol abusive content are helping Twitter better compete for advertising dollars, executives said. Pixabay

“People are not impressed with a made up metric and their reluctance to give us actual users,” said analyst Michael Pachter at Wedbush Securities. “I don’t think the stock can get out of its own way until they come clean and report the same metrics everyone else does.”

Forecast largely below Wall Street

For the first quarter, Twitter’s revenue rose 18 percent to $787 million from the year-ago quarter, topping analyst estimates of $776.1 million.

But Twitter also forecast revenue for the second quarter largely below analyst estimates, and said that it would continue to spend heavily on cleaning up Twitter as well as new ad products.

Ad sales jumped 18 percent to $679 million. In the United States, ad revenue rose by 26 percent. Total operating expense including cost of revenue rose by 18 percent from the first quarter a year ago. The company reiterated that operating expenses would grow about 20 percent in 2019.

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For the first quarter, Twitter’s revenue rose 18 percent to $787 million from the year-ago quarter, topping analyst estimates of $776.1 million. Pixabay

Twitter reported quarterly profit of $191 million, or 25 cents a share, compared with $61 million, or 8 cents per share, a year earlier. Excluding a $124.4 million tax benefit, the company earned 9 cents per share.

ALSO READ: Manish Maheshwari to be the Managing Director of Twitter India

The results appeared to catch the attention of U.S. President Donald Trump, who called for the creation of “more, and fairer” social media companies, repeating his claim that Twitter is biased against Republicans, without presenting evidence.

“We enforce the Twitter Rules dispassionately and equally for all users, regardless of their background or political affiliation,” a Twitter representative said. “We are constantly working to improve our systems and will continue to be transparent in our efforts.” (VOA)

Next Story

Researchers Develop New Algorithm to Identify Cyber-bullies on Twitter

“In a nutshell, the algorithms ‘learn’ how to tell the difference between bullies and typical users by weighing certain features as they are shown more examples,” said Blackburn

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FILE - A man reads tweets on his phone in front of a displayed Twitter logo. VOA

Researchers have developed machine learning algorithms which can identify bullies and aggressors on Twitter with 90 per cent accuracy.

For the study published in the journal Transactions on the Web, the research team analysed the behavioural patterns exhibited by abusive Twitter users and their differences from other users.

“We built crawlers — programs that collect data from Twitter via variety of mechanisms,” said study researcher Jeremy Blackburn from Binghamton University in the US.

“We gathered tweets of Twitter users, their profiles, as well as (social) network-related things, like who they follow and who follows them,” Blackburn said.

The researchers then performed natural language processing and sentiment analysis on the tweets themselves, as well as a variety of social network analyses on the connections between users.

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Twitter is a social media app that encourages short tweets and brief conversations. Pixabay

They developed algorithms to automatically classify two specific types of offensive online behaviour, i.e. cyber-bullying and cyber-aggression.

The algorithms were able to identify abusive users — who engage in harassing behaviour like those who send death threats or make racist remarks — on Twitter with 90 per cent accuracy.

Also Read: Facebook Announces Some New Features to its Video Capabilities

“In a nutshell, the algorithms ‘learn’ how to tell the difference between bullies and typical users by weighing certain features as they are shown more examples,” said Blackburn.

“Our research indicates that machine learning can be used to automatically detect users that are cyber-bullies, and thus could help Twitter and other social media platforms remove problematic users,” Blackburn added. (IANS)