Twitter officially launches labels to identify the ‚good bots‘

The revised policy now allows the use of the Twitter API for academic research purposes. In addition, Twitter is simplifying its rules around the redistribution of Twitter data to aid researchers. Now, researchers will be able to share an unlimited number of Tweet IDs and/or User IDs, if they’re doing so on behalf of an academic institution and for the sole purpose of non-commercial research, such as peer review, says Twitter.

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Underneath the account’s name and @username, a small robot icon appears next to the words “Automated by” followed by the name of the account’s operator. Aside from search engine bots, there are other crawlers that perform specific useful tasks. There can be both good scrapers and malicious scrapers, e.g. ones that steal content from a publisher. For the most part, good crawlers will declare themselves — e.g. moatbot, pinterest-bot, etc. — in the user agent. These are declared bots that come for various reasons to the site. They are bots, but not marked red, because they still “say their name honestly” when visiting the page.

However, for advertisers, even a useful search crawler is still a bot. And an ad shown to a good bot is still not useful to the advertiser. Most ad exchanges and ad servers know to block search engine crawlers, using a list of bot names. But often you’re not sure, and IVT detection may record this as part of their G-IVT (general-IVT) bot line. However it’s not clear how many automated accounts will take up the offer, or whether the owners of many of these accounts would want to advertise that they are not run by humans. Yet some automated accounts are seen by Twitter as having a positive impact on the platform.

The company believes the labels will increase the legitimacy of such accounts and build trust and transparency with their audiences. Twitter had previewed the system in May, in an attempt to give people more information to differentiate automated from human-run accounts. The company gives several examples of „good bots“ including accounts that share vaccination updates, information about seismic activity or material from public museums. „The developers that have been successful, which is true of the platform in general but particularly in social good and humanitarian good, is that they embrace that it’s a living, breathing platform,“ he says. So she teamed up with Brad Jacobson, senior experience strategist at R/GA.

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Examples of automated accounts you might see on Twitter include bots that help you find vaccine appointments and disaster early warning systems. When these accounts let you know they’re automated, you get a better understanding of their purpose when you’re interacting with them. Electrocomponents is currently undergoing a company-wide series of cloud migration projects.

As we covered last year, accounts defined as Good Bots can be identified by a robot icon followed by the label “Automated” just above a tweet or profile. There, users can also find information about the developer who created that bot account. #GoodBots help people stay apprised of useful, entertaining, and relevant information from fun emoji mashups to breaking news. Starting today, all automated accounts will have the option to add a new label to their account Profile. The label will give people on Twitter additional information about the bot and its purpose to help them decide which accounts to follow, engage with, and trust.

• One user of Ask for a Raise shared an anecdote with R/GA about how, after using this bot, she walked into her boss‘ office and got the raise she deserved.
• You just send one command to the botnet to visit a list of sites, a specific number of times.
• These are the determined and clever enemies you are up against in your digital ad spending.
• As examples of good bots, Twitter pointed to the fun account @everycolorbot and informative @earthquakesSF.
• Javed, for one, wants to turn Tarjimly into a full business with social good in its DNA.
• In order to identify whether an account is a “Good Bot” on Twitter, you can look for a robot icon followed by the label “Automated” just above the tweet or profile.

Tarjimly isn’t alone in taking on a huge issue like the global refugee crisis through Messenger. UNICEF’s U-Report, an early example of a Messenger bot launched in August 2016, allows young people around the world to answer weekly questions on issues that affect them. UNICEF chose Messenger because it wanted to tap into the youth demographic in order to advocate for children’s rights, noting that young people are more likely to engage on channels they’re already using. He and his cofounders had made bots for Facebook Messenger before and knew it would ease the process for people who wanted to register for the service, rather than forcing them to adopt a separate app.

Of course, those operating bots for more nefarious purposes — like spreading propaganda or disinformation — will likely just ignore this policy and hope not to be found out. This particular change follows the recent finding that a quarter of all tweets about climate change were coming from bots posting messages of climate change denialism. In addition, it was recently discovered that Trump supporters and QAnon conspiracists were using an app called Power10 to turn their Twitter accounts into bots. Carter and Jacobson continued iterating over time, looking at how people interacted with the bot and accounting for responses they weren’t prepared for. They both agree that chatbots aren’t just useful because they automate processes—they’re perfect for personal moments.

• Again there were no bots hitting the non-existent pages on mainstream sites.
• Carter describes it as „creating a conversation between two women that gives you both what you’re looking for.“
• Facebook opened up the Messenger platform to developers last year, and since then more than 100,000 unique bots have been created.
• The company gives several examples of „good bots“ including accounts that share vaccination updates, information about seismic activity or material from public museums.
• Or the malware can just commingle its activity with the humans’ activity on the device, making it nearly impossible for fraud detection to distinguish the real human from the bot, made from malware hidden on the device.
• Twitter has been under fire in the past for its rampant bot problem.

The publisher can then choose to block them, if their activity is unwanted on the site. Finally, the revamped policy clarifies that not all bots are bad. Some even enhance the Twitter experience, the company says, or provide useful information. As examples of good bots, Twitter pointed to the fun account @everycolorbot and informative @earthquakesSF. The company is also revising rules to clarify how developers are to proceed when the use cases for Twitter data change. In the new policy, developers are informed that they must notify the company of any “substantive” modification to their use case and receive approval before using Twitter content for that purpose.

These are the determined and clever enemies you are up against in your digital ad spending. So should you assume your campaigns are “fraud free” even if trade associations and your own agencies tell you “don’t worry about it; we’ve got fraud detection in place? Instead in both of these cases, there were no bots going to the mainstream sites. Fraudsters were simply pumping billions of faked bid requests into the exchanges and declaring the domain or webpage url to be coming from major publishers’ domains, to trick buyers into bidding. They did; and this simple domain-spoofing con netted the fraudsters more money, without even having to send any bots to any websites at all. Fraudsters were making money, and the two fraud detection companies didn’t even understand how the con worked.

BBC News Services

Once the publisher can see the amount of bots with analytics, and also see what they are and where they came from, the publisher can take steps. For example, good publishers would filter and block these bots so that advertisers who place ads on their sites won’t be exposed to these bots — i.e. no ads are called when these bots are on the site. Sites can also use this data to filter out bot activity and make their KPIs more accurate — e.g. conversion rates, click rates, etc. A study by Carnegie Mellon University, external last year found that nearly half of the Twitter accounts spreading messages on the social media platform about the coronavirus pandemic were likely automated accounts.