Fake News are now a hot issue in Europe as well as worldwide, particularly referred to Political and Social Challenges that reflect in business as well as in industry. Europe is lacking of a systematic knowledge and data transfer across organizations to address the aggressive emergence of the well-known problem of fake news and post-truth effect. The possibility to use cross sector Big Data management and analytics, along with an effective interoperability scheme for all our data sources, will tackle this urgent problem, generating new business and societal impacts involving several stakeholders: a) Media Companies: news agencies, broadcaster, newspapers, etc, b) Governmental institutions and organisations, c) The overall industrial ecosystem, d) The entire society. The aim of FANDANGO is to aggregate and verify different typologies of news data, media sources, social media, open data, so as to detect fake news and provide a more efficient and verified communication for all European citizens. European tradition in democracy, journalism and transparency should play a wordwide example in fast changing society, where all citizens appears completely overwhelmed by the new technologies and by the new social challenges. The FANDANGO project aims to break data interoperability barriers providing unified techniques and an integrated big data platform to support traditional media industries to face the new “data” news economy with a better transparency to the citizens under a Responsible, Research and Innovation prism. This goal will be validated and tested in three specific domains Climate, Immigration and European Context, these are typical scenarios where fake news can influence perception with respect to social and business actions and where news can be verified and validated by trustable information, based on facts and data.

MixedEmotions will develop innovative multilingual multi-modal Big Data analytics applications that will analyze a more complete emotional profile of user behavior using data from mixed input channels: multilingual text data sources, A/V signal input (multilingual speech, audio, video), social media (social network, comments), and structured data. Commercial applications (implemented as pilot projects) will be in Social TV, Brand Reputation Management and Call Centre Operations. Making sense of accumulated user interaction from different data sources, modalities and languages is challenging and has not yet been explored in fullness in an industrial context. Commercial solutions exist but do not address the multilingual aspect in a robust and large-scale setting and do not scale up to huge data volumes that need to be processed, or the integration of emotion analysis observations across data sources and/or modalities on a meaningful level. MixedEmotions will implement an integrated Big Linked Data platform for emotion analysis across heterogeneous data sources, different languages and modalities, building on existing state of the art tools, services and approaches that will enable the tracking of emotional aspects of user interaction and feedback on an entity level. The MixedEmotions platform will provide an integrated solution for: large-scale emotion analysis and fusion on heterogeneous, multilingual, text, speech, video and social media data streams, leveraging open access and proprietary data sources, and exploiting social context by leveraging social network graphs; semantic-level emotion information aggregation and integration through robust extraction of social semantic knowledge graphs for emotion analysis along multidimensional clusters.

Intelligence Network & Secure Platform for Evidence Correlation and Transfer (INSPECTr). The principal objective of INSPECTr will be to develop a shared intelligent platform and a novel process for gathering, analysing, prioritising and presenting key data to help in the prediction, detection and management of crime in support of multiple agencies at local, national and international level. This data will originate from the outputs of free and commercial digital forensic tools complemented by online resource gathering . Using both structured and unstructured data as input, the developed platform will facilitate the ingestion and homogenisation of this data with increased levels of automisation, allowing for interoperability between outputs from multiple data formats. Various knowledge discovery techniques will allow the investigator to visualise and bookmark important evidential material and export it to an investigative report. In addition to providing basic and advanced (cognitive) cross-correlation analysis with existing case data, this technique will aim to improve knowledge discovery across exhibit analysis within a case, between separate cases and ultimately, between interjurisdictional investigations. INSPECTr will deploy big data analytics, cognitive machine learning and blockchain approaches to significantly improve digital and forensics capabilities for pan-European LEAs. INSPECTr intends to reduce the complexity and the costs in law enforcement agencies and related actors to use leading edge analytical tools proportionally and in line with relevant legislation (including fundamental rights), with extended options for multi-level and cross-border collaboration for both reactive and preventive policing and facilitate the detection/prediction of cybercrime operations/trends. The final developed platform will be freely available to all LEAs.