How can rumors and truth be connected

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Materials and Methods The way the data is collected as well as the manual annotation process constitute an important part of our methodology, allowing us to analyse conversational features of social media. Data Collection Previous research has focused on collecting data for rumours known to have been circulating a priori [ 11 , 22 , 23 ] rather than a more general approach to the collection of new rumours.

Collection of Rumourous Event Data. We collected tweets for nine different events, which include five cases of breaking news: Ferguson unrest : citizens of Ferguson in Michigan, USA, protested after the fatal shooting of an year-old African American, Michael Brown, by a white police officer on August 9, Charlie Hebdo shooting : two brothers forced their way into the offices of the French satirical weekly newspaper Charlie Hebdo in Paris, killing 11 people and wounding 11 more, on January 7, The plane was ultimately found to have been deliberately crashed by the co-pilot of the plane.

And four specific rumours known a priori : Prince to play in Toronto : a rumour started circulating on November 3, that the singer Prince would play a secret show in Toronto that night.

Some people even queued at the venue to attend the concert, but the rumour was later proven false. Gurlitt collection : a rumour in November that the Bern Museum of Fine Arts was going to accept a collection of modernist masterpieces kept by the son of a Nazi-era art dealer.

The museum did end up accepting the collection, confirming the rumours. Putin missing : numerous rumours emerged in March when the Russian president Vladimir Putin did not appear in public for 10 days. He spoke on the 11th day, denying all rumours that he had been ill or was dead.

The report was later denied by the footballer and thus exposed as a hoax. Data Sampling. Data Annotation. Download: PPT. Outcome of the Rumour Annotation Task.

Complementing the Dataset with Conversations. Annotation of Rumourous Conversations. Each tweet in a rumourous conversation is annotated in terms of the above three dimensions, which we define in detail below: Support and Response Type : Support : Support is only annotated for source tweets.

It defines if the message in a source tweet is conveyed as a statement that supports or denies the content of the statement. Response Type : Response Type is used to designate the support of response tweets towards a source tweet that introduces a rumourous story. Some responses can be very helpful in determining the veracity of a rumour, and thus we annotate Response Type with one of the following four values: 1 Agreed , when the author of the response supports the statement they are responding to, 2 Disagreed , when the author of the response disagrees with the statement they are responding to, 3 Appeal for more information , when the author of the response asks for additional evidence in relation to the statement they are responding to, or 4 Comment , when the author of the response makes their own comment without a clear contribution to assessing the veracity of either the tweet they are responding to or the source tweet.

The inclusion of the Response Type dimension in the annotation scheme follows Procter et al. However, unlike [ 22 ] we additionally consider the annotation of Response Type for nested responses, i. In this case Response Type is annotated for two different aspects: i the type of response with respect to the rumour in the source tweet and ii the type of response towards its parent tweet, i.

This double annotation allows us to better analyse the way conversations flow and how opinions evolve with respect to veracity. It is worth noting that the response type is not necessarily transitive, and the aggregation of pairwise agreements and disagreements with previous tweets does not necessarily match with the agreement with the source.

Fig 4 shows an example of the double annotation, and how we determine the support towards the rumour. Certainty : Certainty measures the degree of confidence expressed by the author of a tweet when posting a statement in the context of a rumour.

It applies to both source tweets and response tweets. The value annotated for either Support or Response Type has no effect on the annotation of Certainty, and thus it is coded regardless of the statement supporting or denying the rumour. The values for Certainty include: 1 Certain , when the author is fully confident or the author is not showing any kind of doubt, 2 Somewhat certain , when they are not fully confident and 3 Uncertain , when the author is clearly unsure.

Evidentiality : Evidentiality determines the type of evidence if any provided by an author of a tweet and applies to both source tweets and response tweets. It is important to note that the evidence provided has to be directly related to the rumour being discussed in the conversation and any other kind of evidence that is irrelevant in that context is not annotated here. Hence, we cater for the fact that a tweet can provide more than one type of evidence, e.

Fig 3. Annotation scheme for rumourous social media conversations. Table 2. Inter-annotator agreement values for different features and tweet types. Results We begin by investigating the diffusion of rumours in our corpus and then move on to analyse the annotations for Support, Certainty, and Evidentiality for both source and response tweets. Rumour Timelines Fig 5 shows the timelines for the rumours collected and annotated for the nine events in our dataset.

Rumour Diffusion To complement the analysis enabled by the visualisation of rumour timelines, we take a closer look at the diffusion of these rumours in the form of retweets. We colour these connections based on the accuracy of the original tweets being retweeted: Blue accurate retweets : the retweets of tweets that are either supporting true rumours, or denying false rumours.

Brown inaccurate retweets : the retweets of tweets that are wrong, i. Orange unverified retweets : the retweets of tweets that still have an unverified status. Table 3. Percentages of retweets for unverified, accurate, and inaccurate tweets. Fig 8. Retweet timelines showing the percentage of retweets that each type of tweet gets in 15 minute steps. Rumour Support and Denial The crowdsourced annotations, which manually categorise each of the source and response tweets according to the type of support expressed with respect to the rumour, enable us to analyse the performance of social media users in terms of support and denial of rumours.

Rumour Certainty Certainty, as we annotated it in the context of rumours, measures the degree of confidence expressed by the author of a tweet. Given these two values, the certainty ratio is computed as: 4 Fig 14 shows the distributions of certainty ratios by veracity status true, false, unverified.

Rumour Evidentiality Evidentiality in the context of a rumour determines the type of evidence if any provided by an author of a tweet. Given these two values, we compute the evidentiality ratio as: 5 Fig 15 shows the distributions of evidentiality ratios by veracity status.

Analysis of Users Next, we examine the role that different users play in the diffusion and support of rumours. Fig Analysis of support, certainty and evidentiality by follow ratio. Discussion and Conclusions The methodology we have developed for collecting, identifying, and annotating rumour threads has enabled us to analyse a number of important aspects of how people react to rumours using measures of diffusion and in the context of responses that either support or deny the veracity of the rumours.

To summarise what has come out of the study results: True rumours tend to be resolved faster than false rumours. References 1. What is Twitter, a social network or a news media? ACM; Silverman C. Verification handbook. Zubiaga A, Ji H. Tweet, but verify: epistemic study of information verification on Twitter.

Social Network Analysis and Mining. View Article Google Scholar 4. Accessed: Derczynski L, Bontcheva K. Pheme: Veracity in digital social networks. Proceedings of ISA. DiFonzo N, Bordia P. Rumor, gossip and urban legends. View Article Google Scholar 8. Towards Detecting Rumours in Social Media. Donovan P. How idle is idle talk? One hundred years of rumor research. View Article Google Scholar Rumor and gossip research. Psychological Science Agenda.

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ArXiv e-prints. FactBank: A corpus annotated with event factuality. Language resources and evaluation. Enabling social media research through citizen social science. PHEME rumour scheme dataset: journalism use case. Wilcoxon F. Individual comparisons by ranking methods. But when that drama involves toxic friendships , slut-shaming , and spreading rumors, that is anything but normal. In fact, for those who are impacted, gossip can be downright painful and almost impossible to ignore — especially if social media is being used to spread it.

Consequently, kids who are being gossiped about are negatively impacted. Gossip and rumors can alienate friends, ruin reputations, and even lead to ostracizing behavior and other forms of relational aggression.

It also helps to understand why kids engage in gossiping and rumor spreading. Rumors are pieces of information or a story that has not been verified. What this means, is that the person telling the story does not know for certain if it is true or not. Most of the time, people who spread rumors do not bother to determine if there is any truth to what they are saying. Typically, rumors are spread from person to person and can change slightly each time they are told.

As a result, they can become exaggerated and altered over time. Rumors can involve just about any topic and often run the gamut. For instance, at school, there could be rumors about casting calls in the theater department, about how the final will be handled in history class, or that the head cheerleader is secretly dating a member of the chess club. Gossip is slightly different from a rumor. Usually, gossip involves a juicy detail of some sort, which means the information is shocking or personal.

Gossip usually involves love, relationships, sex, and other issues that people usually do not talk about publicly. Additionally, gossip almost always causes pain and humiliation for the person it is about. People share gossip without any thought of how it might impact the person it is about.

There are a variety of reasons why kids will spread rumors or engage in gossip. The implications seem clear, though they can only be made official through further experimentation. At present the researchers have established that false news propagates faster, and false news is more novel and negative. Another experiment will have to prove that false news propagates faster because it is more novel and negative.

If humans are responsible for the spread of false news, what hope do we have? Just maybe not on this scale. Roy said he liked to frame the question as one of health. Roy and others are working on building what he called health indicators for a system like Twitter, but obviously also for other online systems — Facebook, Instagram, forums, you name it. But he was quick to point out that those platforms are just part of what you might call a holistic online health approach.

If the platform becomes a wasteland of false news and unhealthy conversations, people may lose interest altogether. I think Facebook and Twitter have a true long-term profit maximizing incentive. But if the problem is with people as well as algorithms and ad rates, what can be done?