The future of work will depend highly on soft skills. No matter how AI for recruitment and talent assessment is leveraged in the future, a candidate's high-order thinking and EQ will stay vital, something which the robots simply can't replace or automate! This accurate AI-powered tool (beyond IBM Watson) gives you full picture of a candidate's soft skill background (based on the Big 5 personality test, DISC OCEAN, mood graphs, sentiment analysis, digital footprint analysis, behavior score, and much more) to help recruiters spot and process the right'candidates' who would add to their diverse, inclusive company culture. Get a free assessment report, at: https://frrole.ai/deepsense-app/ You just need the twitter handle/ email ID of the individual to get started.

This is part IV of our tour through the papers from the Re-coding Black Mirror workshop exploring future technology scenarios and their social and ethical implications. In 2016, the world witnessed the storming of social media by social bots spreading fake news during the US Presidential elections… researchers collected Twitter data over four weeks preceding the final ballot to estimate the magnitude of this phenomenon. Their results showed that social bots were behind 15% of all accounts and produced roughly 19% of all tweets… What would happen if social media were to get so contaminated by fake news that trustworthy information hardly reaches us anymore? Fake news and hoaxes have been around a long time, but the nature of social media pours fuel on the fire. Any user can create and relay content with little third-party filtering or fact-checking; many adults get their news on social media; and research has shown that people exposed to fake news tend to believe it.

This track includes data-related tasks such as analysis, capture, curation, search, sharing, storage, transfer, visualization, and information privacy, with special focus on social data on the web. Hence, the broader context of the track comprehends AI, web mining, information retrieval, natural language processing, and sentiment analysis. As the web rapidly evolves, web users are evolving with it. In an era of social connectedness, people are becoming increasingly enthusiastic about interacting, sharing, and collaborating through social networks, online communities, blogs, wikis, and other online collaborative media. In recent years, this collective intelligence has spread to many different areas, with particular focus on fields related to everyday life such as commerce, tourism, education, and health, causing the size of the social web to expand exponentially. The distillation of knowledge from such a large amount of unstructured information, however, is an extremely difficult task, as the contents of today’s web are perfectly suitable for human consumption, but remain hardly accessible to machines. The opportunity to capture the opinions of the general public about social events, political movements, company strategies, marketing campaigns, and product preferences has raised growing interest both within the scientific community, leading to many exciting open challenges, as well as in the business world, due to the remarkable benefits to be had from marketing and financial market prediction. The primary aim of this track is exploring the new frontiers of big data computing for opinion mining and sentiment analysis through machine learning techniques, knowledge-based systems, adaptive and transfer learning, in order to more efficiently retrieve and extract social information from the web.

Event Extraction (EE) is a challenging Information Extraction task which aims to discover event triggers of specific types along with their arguments. Most recent research on Event Extraction relies on pattern-based or feature-based approaches, trained on annotated corpora, to recognize combi- nations of event triggers, arguments, and other contextual in- formation. However, as the event instances in the ACE corpus are not evenly distributed, some frequent expressions involving ACE event triggers do not appear in the training data, adversely affecting the performance. In this paper, we demon- strate the effectiveness of systematically importing expert-level patterns from TABARI to boost EE performance. The experimental results demonstrate that our pattern-based sys- tem with the expanded patterns can achieve 69.8% (with 1.9% absolute improvement) F-measure over the baseline, an advance over current state-of-the-art systems.

We seek to determine the effectiveness of using location-based social media to predict the outcome of the 2016 presidential election. To this aim, we create a dataset consisting of approximately 3 million tweets ranging from September 22nd to November 8th related to either Donald Trump or Hillary Clinton. Twenty-one states are chosen, with eleven categorized as swing states, five as Clinton favored and five as Trump favored. We incorporate two metrics in polling voter opinion for election outcomes: tweet volume and positive sentiment. Our data is labeled via a convolutional neural network trained on the sentiment140 dataset. To determine whether Twitter is an indicator of election outcome, we compare our results to the election outcome per state and across the nation. We use two approaches for determining state victories: winner-take-all and shared elector count. Our results show tweet sentiment mirrors the close races in the swing states; however, the differences in distribution of positive sentiment and volume between Clinton and Trump are not significant using our approach. Thus, we conclude neither sentiment nor volume is an accurate predictor of election results using our collection of data and labeling process.

This track is intended to present works ranking from logical, mathematical, and statistical models in syntax, semantics (logic of objects, topological theories of time and space, lexical associations, etc.) and discourse as foundations of the design and analysis to knowledge processing and natural language processing systems and especially to information extraction.

The U.S. Justice Department and the FBI are investigating Cambridge Analytica, a now-defunct political data firm embroiled in a scandal over its handling of Facebook Inc user information. Prosecutors have sought to question former Cambridge Analytica employees and banks that handled its business, the newspaper said, citing an American official and others familiar with the inquiry. Cambridge Analytica said earlier this month it was shutting down after losing clients and facing mounting legal fees resulting from reports the company harvested personal data about millions of Facebook users beginning in 2014. Allegations of the improper use of data for 87 million Facebook users by Cambridge Analytica, which was hired by President Donald Trump's 2016 U.S. election campaign, have prompted multiple investigations in the United States and Europe. The investigation by the Justice Department and FBI appears to focus on the company's financial dealings and how it acquired and used personal data pulled from Facebook and other sources, the Times said.

Three million Facebook users had their most intimate details exposed as a new data protection scandal hits the social media platform. In the latest of a string of security breaches, a report from New Scientist has revealed a popular personality app insufficiently protected the'anonymous' data of participants. The quiz, called myPersonality, collected highly sensitive data, including psychometric test results that revealed how neurotic or extrovert an individual was. The investigation found the information was poorly protected for four years and gaining access to was relatively easy. Three million of Mark Zuckerberg's Facebook users had intimate details exposed as a new data protection scandal has hit the social media platform.

Data from millions of Facebook users who used a popular personality app, including their answers to intimate questionnaires, was left exposed online for anyone to access, a New Scientist investigation has found. Academics at the University of Cambridge distributed the data from the personality quiz app myPersonality to hundreds of researchers via a website with insufficient security provisions, which led to it being left vulnerable to access for four years. Gaining access illicitly was relatively easy. The data was highly sensitive, revealing personal details of Facebook users, such as the results of psychological tests. It was meant to be stored and shared anonymously, however such poor precautions were taken that deanonymising would not be hard.

At least 200 apps have been suspended from Facebook amidst a data privacy investigation launched by Mark Zuckerberg after the Cambridge Analytica scandal in March. On Monday, Facebook announced its internal investigation was in "full swing" -- with teams delving into thousands of apps that are connected to Facebook, according to a statement released by Ime Archibong, vice president of Facebook's product partnerships. Facebook's investigation has already led to the suspension of around 200 apps which will be analyzed to see "whether they did in fact misuse any data." Archibong said the second phase of the investigation involves looking into whether there is evidence that the suspended apps or other apps misused data. If an app misled users in how their data was being used, it could be banned from Facebook.