Subjective questions such as does neymar dive', or is clinton lying', or is trump a fascist', are popular queries to web search engines, as can be seen by autocompletion suggestions on Google, Yahoo and Bing. In the era of cognitive computing, beyond search, they could be handled as hypotheses issued for evaluation. Our vision is to leverage on unstructured data and metadata of the rich user-generated multimedia that is often shared as material evidence in favor or against hypotheses in social media platforms. In this paper we present two preliminary experiments along those lines and discuss challenges for a cognitive computing system that collects material evidence from user-generated multimedia towards aggregating it into some form of collective decision on the hypothesis.
. Our work is related to the so-called debating' technologies at IBM @cite_2 . Their work is strongly based on machine learning methods to track correlated text passages that may be spread over a corpus. Going beyond the observational hypotheses that are more on our sight and are typical of social media, the goal is to enable eliciting all kind of textual information that may be relevant as evidence to decide on a claim. An example is: (topic) ''; (claim) ''; (evidence) .'' To date, as far as we know this is yet a fully NLP project, which does not consider multimedia data for grounding on material evidence.
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Deciding whether a claim is true or false often requires understanding the evidence supporting and contradicting the claim. However, when learning about a controversial claim, human biases and viewpoints may affect which evidence documents are considered "trustworthy" or credible. It is important to overcome this bias and know both viewpoints to get a balanced perspective. In this paper, we study various factors that affect learning about the truthfulness of controversial claims. We designed a user study to understand the impact of these factors. Specifically, we studied the impact of presenting evidence with contrasting viewpoints and source expertise rating on how users accessed the evidence documents. This would help us optimize how to teach users about controversial topics in the most effective way, and to design better claim verification systems. We find that users do not seek contrasting viewpoints by themselves, but explicitly presenting contrasting evidence helps them get a well-rounded understanding of the topic. Furthermore, explicit knowledge of the source credibility and the context not only affects what users read, but also how credible they perceive the document to be. Engaging in a debate with oneself or others to take decisions is an integral part of our day-today life. A debate on a topic (say, use of performance enhancing drugs) typically proceeds by one party making an assertion claim (say, PEDs are bad for health) and then providing an evidence to support the claim (say, a 2006 study shows that PEDs have psychiatric side effects). In this work, we propose the task of automatically detecting such evidences from unstructured text that support a given claim. This task has many practical applications in decision support and persuasion enhancement in a wide range of domains. We first introduce an extensive benchmark data set tailored for this task, which allows training statistical models and assessing their performance. Then, we suggest a system architecture based on supervised learning to address the evidence detection task. Finally, promising experimental results are reported. Deciding whether a claim is true or false often requires a deeper understanding of the evidence supporting and contradicting the claim. However, when presented with many evidence documents, users do not necessarily read and trust them uniformly. Psychologists and other researchers have shown that users tend to follow and agree with articles and sources that hold viewpoints similar to their own, a phenomenon known as confirmation bias. This suggests that when learning about a controversial topic, human biases and viewpoints about the topic may affect what is considered "trustworthy" or credible. It is an interesting challenge to build systems that can help users overcome this bias and help them decide the truthfulness of claims. In this article, we study various factors that enable humans to acquire additional information about controversial claims in an unbiased fashion. Specifically, we designed a user study to understand how presenting evidence with contrasting viewpoints and source expertise ratings affect how users learn from the evidence documents. We find that users do not seek contrasting viewpoints by themselves, but explicitly presenting contrasting evidence helps them get a well-rounded understanding of the topic. Furthermore, explicit knowledge of the credibility of the sources and the context in which the source provides the evidence document not only affects what users read but also whether they perceive the document to be credible. Language: en The massive spread of fake news has been identified as a major global risk and has been alleged to influence elections and threaten democracies. Communication, cognitive, social, and computer scientists are engaged in efforts to study the complex causes for the viral diffusion of digital misinformation and to develop solutions, while search and social media platforms are beginning to deploy countermeasures. However, to date, these efforts have been mainly informed by anecdotal evidence rather than systematic data. Here we analyze 14 million messages spreading 400 thousand claims on Twitter during and following the 2016 U.S. presidential campaign and election. We find evidence that social bots play a key role in the spread of fake news. Accounts that actively spread misinformation are significantly more likely to be bots. Automated accounts are particularly active in the early spreading phases of viral claims, and tend to target influential users. Humans are vulnerable to this manipulation, retweeting bots who post false news. Successful sources of false and biased claims are heavily supported by social bots. These results suggests that curbing social bots may be an effective strategy for mitigating the spread of online misinformation.