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| Trump's Twitter debate lead was 'swelled by bots' | Trump's Twitter debate lead was 'swelled by bots' |
| (about 1 hour later) | |
| More than four times as many tweets were made by pro-Donald Trump bots in and around the first US presidential debate as the number made by those backing Hillary Clinton, a study found. | |
| The research indicates the Republican candidate would have enjoyed more support on Twitter even if the automated accounts had not been active. | The research indicates the Republican candidate would have enjoyed more support on Twitter even if the automated accounts had not been active. |
| But it highlights that the software has the capacity to "manipulate public opinion" and "muddy political issues". | But it highlights that the software has the capacity to "manipulate public opinion" and "muddy political issues". |
| The report has yet to be peer-reviewed. | The report has yet to be peer-reviewed. |
| And one critic noted that it was impossible to be completely sure which accounts were real and which were "web robots". | And one critic noted that it was impossible to be completely sure which accounts were real and which were "web robots". |
| Profuse tweeters | Profuse tweeters |
| The investigation was led by Prof Philip Howard, from the University of Oxford, and is part of a wider project exploring "computational propaganda". | The investigation was led by Prof Philip Howard, from the University of Oxford, and is part of a wider project exploring "computational propaganda". |
| It covered tweets posted on 26 September, the day of the debate, plus the three days afterwards, and relied on popular hashtags linked to the event. | It covered tweets posted on 26 September, the day of the debate, plus the three days afterwards, and relied on popular hashtags linked to the event. |
| Examples of pro-Trump hashtags tracked by the study: | Examples of pro-Trump hashtags tracked by the study: |
| Examples of pro-Clinton hashtags tracked by the study: | Examples of pro-Clinton hashtags tracked by the study: |
| First, the researchers identified accounts that exclusively posted messages containing hashtags associated with one candidate but not the other. | First, the researchers identified accounts that exclusively posted messages containing hashtags associated with one candidate but not the other. |
| These accounted for about 1.8 million pro-Trump tweets and 613,000 pro-Clinton posts. | These accounted for about 1.8 million pro-Trump tweets and 613,000 pro-Clinton posts. |
| The researchers then analysed which of these had been posted by bots. They identified an account as such if it had tweeted at least 50 times a day across the period, meaning a minimum of 200 tweets over the four days. | The researchers then analysed which of these had been posted by bots. They identified an account as such if it had tweeted at least 50 times a day across the period, meaning a minimum of 200 tweets over the four days. |
| The results suggested that 32.7% of such pro-Trump tweets had been posted by bots and 22.3% of such pro-Clinton ones. | The results suggested that 32.7% of such pro-Trump tweets had been posted by bots and 22.3% of such pro-Clinton ones. |
| In total, that represented a total of 576,178 tweets benefiting the Republican nominee and 136,639 in support of the Democratic one. | In total, that represented a total of 576,178 tweets benefiting the Republican nominee and 136,639 in support of the Democratic one. |
| "On the balance of probabilities, if you pulled out a heavily automated account the odds are four to one that you'll find it's a bot tweeting in favour of Trump," said Prof Howard. | "On the balance of probabilities, if you pulled out a heavily automated account the odds are four to one that you'll find it's a bot tweeting in favour of Trump," said Prof Howard. |
| There is no suggestion, however, that bots were generated by either of the official Presidential campaign groups. | There is no suggestion, however, that bots were generated by either of the official Presidential campaign groups. |
| "We are not looking at the source, who is working on the bots or to what end, merely the metrics of the data," said Prof Howard. | "We are not looking at the source, who is working on the bots or to what end, merely the metrics of the data," said Prof Howard. |
| Looking wider - to accounts that tweeted neutral hashtags or a mix of different kinds - the study suggested that 23% of all the tweets were driven by bots. | Looking wider - to accounts that tweeted neutral hashtags or a mix of different kinds - the study suggested that 23% of all the tweets were driven by bots. |
| One machine learning expert cautions that the criteria used to identify the bots might have been too imprecise to have sifted out all the human-based activity. | One machine learning expert cautions that the criteria used to identify the bots might have been too imprecise to have sifted out all the human-based activity. |
| "Real people can write a script and use an algorithm to tweet regularly with specific responses, or humans can tweet content that looks almost identical to a series of bots flooding a political hashtag", comments Caroline Sinders, an ex-IBM researcher who now works for Buzzfeed. | "Real people can write a script and use an algorithm to tweet regularly with specific responses, or humans can tweet content that looks almost identical to a series of bots flooding a political hashtag", comments Caroline Sinders, an ex-IBM researcher who now works for Buzzfeed. |
| "Also, political commentators or people eagerly engaged in the political debate could also tweet this many times." | "Also, political commentators or people eagerly engaged in the political debate could also tweet this many times." |
| So, is it possible that Trump supporters might simply have been more enthusiastic than Clinton's and have done a better job at leveraging social media to their advantage? | So, is it possible that Trump supporters might simply have been more enthusiastic than Clinton's and have done a better job at leveraging social media to their advantage? |
| Prof Howard said that it is unlikely to be the only explanation. | Prof Howard said that it is unlikely to be the only explanation. |
| "Most of the heavy automation and tweets happened overnight and shared similar hashtags and information," he explains. | "Most of the heavy automation and tweets happened overnight and shared similar hashtags and information," he explains. |
| "They show behaviour that is not human and often don't have comments [about other issues apart from] the particular topic in question." | "They show behaviour that is not human and often don't have comments [about other issues apart from] the particular topic in question." |
| He adds that the 50-tweets-a-day rule was borne out by analysis of posts made during a past Venezuelan election and the Brexit vote. | He adds that the 50-tweets-a-day rule was borne out by analysis of posts made during a past Venezuelan election and the Brexit vote. |
| In both cases, his team double-checked a sample of accounts that had been flagged as bots and confirmed they displayed other characteristics of being inhuman. | In both cases, his team double-checked a sample of accounts that had been flagged as bots and confirmed they displayed other characteristics of being inhuman. |
| "From our data most real Twitter users don't get up to 50 times a day," he said. "So, on balance, that benchmark has worked well." | "From our data most real Twitter users don't get up to 50 times a day," he said. "So, on balance, that benchmark has worked well." |
| How to spot a bot | How to spot a bot |
| Bots take on various guises but have some give-away signs. | Bots take on various guises but have some give-away signs. |
| They often do not feature a profile image, and when they do it is often shared among multiple accounts - so watch out for duplicates. | They often do not feature a profile image, and when they do it is often shared among multiple accounts - so watch out for duplicates. |
| Bots also tend to follow many more accounts than than they are followed by in turn - a sign that they do not have real friends or work colleagues. | Bots also tend to follow many more accounts than than they are followed by in turn - a sign that they do not have real friends or work colleagues. |
| They often have little to say apart from the topic of conversation they have been created to post about, and may tweet prolifically without apparent recourse to sleep. | They often have little to say apart from the topic of conversation they have been created to post about, and may tweet prolifically without apparent recourse to sleep. |
| Also watch out for accounts that reply to your messages in less time than was humanly possible to read what you wrote. | Also watch out for accounts that reply to your messages in less time than was humanly possible to read what you wrote. |
| A final giveaway is if scrutiny of the bot's account reveals it has sent the same response to you to dozens of others too. | A final giveaway is if scrutiny of the bot's account reveals it has sent the same response to you to dozens of others too. |
| Hashtags galore | Hashtags galore |
| In addition to being more numerous, Prof Howard also points to the pro-Trump tweets being more effective, whether generated by bots or not. | In addition to being more numerous, Prof Howard also points to the pro-Trump tweets being more effective, whether generated by bots or not. |
| They were more likely to add multiple hashtags and links to relevant web addresses to fill up the available 140-characters, he explains, which in turn helps keep tags alive and bolsters Trump's message. | They were more likely to add multiple hashtags and links to relevant web addresses to fill up the available 140-characters, he explains, which in turn helps keep tags alive and bolsters Trump's message. |
| "Someone wanting to follow [one of the hashtags] will see a lot more content and more cross posting," said the professor. | "Someone wanting to follow [one of the hashtags] will see a lot more content and more cross posting," said the professor. |
| It is unclear what effect the bots actually have on voter behaviour, but Prof Howard believes that negative messages are more likely to have an impact than positive ones. | It is unclear what effect the bots actually have on voter behaviour, but Prof Howard believes that negative messages are more likely to have an impact than positive ones. |
| "In the 2008 and 2012 US elections, bots were used to make politicians look more popular with voters," he said. | "In the 2008 and 2012 US elections, bots were used to make politicians look more popular with voters," he said. |
| "These days it's about engaging with your support base and constantly feeding them information, and certain hashtags that will keep their level of interest high." | "These days it's about engaging with your support base and constantly feeding them information, and certain hashtags that will keep their level of interest high." |
| One silver lining from the study is that humans are still the dominant force on Twitter and for the most part they seek out posts from other people. | One silver lining from the study is that humans are still the dominant force on Twitter and for the most part they seek out posts from other people. |
| "It's affirming that social media platforms can still present spaces where some people can have political conversations with their networks to genuinely discuss their views, " concludes Prof Howard. | "It's affirming that social media platforms can still present spaces where some people can have political conversations with their networks to genuinely discuss their views, " concludes Prof Howard. |
| "But when nearly a quarter of Twitter activity turns out to be automated it can compound the view that politicians are out to manipulate public opinion." | "But when nearly a quarter of Twitter activity turns out to be automated it can compound the view that politicians are out to manipulate public opinion." |