Work Unit 1

Title

Conceptual and methodological approach to project implementation and management

Activity Statement

Feasibility study

Responsible Body

Qix

Schedule: M1-M2

OBJECTIVES: The analysis and evaluation of the potential of the proposed platform, the highlighting of strengths and weaknesses of the solution and the comparison with existing tools and methodologies, the identification of the resources required for its implementation and the definition of the key indicators.

METHODOLOGY / DESCRIPTION: EU1 will identify the functions, data and technologies that can be integrated into the final solution (E1.1). It will identify the strengths and weaknesses, opportunities and threats that arise from the project as well as the users and markets in which it will target and record the competition (E1.2). Finally, it will determine in detail the resources required for the implementation of the project and will define the main performance monitoring indicators (KPIs) of the project (E1.3). The total results of EU1 will be reflected in the Feasibility Study of the project (P.1.1).

TASKS:

E1.1 Conceptual definition of the system

E1.2 Competition analysis

E1.3 Innovation & Business Risk Management Plan

E1.4 Management plan E.1.3 Definition of required resources and key performance indicators (KPIs)

DELIVERY:

P1.1 Feasibility study (Report, M2)

Work Unit 2

Title

Design and implementation of MVP

Activity Statement

ΒΙΕ

Responsible Body

PALO

Schedule: M1-M12

OBJECTIVES: Design and implementation of the graphic aggregation network and its integration into a key product that will model the dissemination of news on social media.

METHODOLOGY / DESCRIPTION: EU2 will utilize corporate reporting and polarity data already available to PALO and will design, implement and train writing models that will model the diffusion of these reports over time on social media (E.2). The section will evaluate different GCNs architectures in real data and the results will be recorded in a relevant report (P2.1). It will then select the best of them and train it with a large amount of data, making the appropriate architectural improvements to improve the model’s ability to predict individual user exposure to a news item or the overall exposure that a news item achieves. depending on the sources that transmit it (E2.2). The basic model that will be trained will be described in terms of its functionality and will be the MVP (P2.2). The resulting research results will be disseminated to the scientific community and the key stakeholders (E.2.3) through the website that will be developed within the project (P2.3), where the relevant publications and technical reports will be uploaded (P.2.3). .2.4) but also any originals (demo).

TASKS:

E2.1 Design and configuration of graph aggregate neural network models for the utilization of business reports and modeling of their dissemination on social media

E2.2 Design a forecast model for news dissemination and measurement of a news report

E2.3 Coordination – Management – Diffusion

DELIVERY:

P2.1 Evaluation of graph synergistic forecast models (Report, M12)

P2.2 Implementation of the MVP (Prototype, M12)

P2.3 Website promoting the results of the project (Website, M12)

P2.4 Report on diffusion activities (Report, M12)

Work Unit 3

Title

Σχεδίαση, υλοποίηση και εκπαίδευση ενιαίου μοντέλου ανίχνευσης astroturfing

Activity Statement

ΒΙΕ

Responsible Body

PALO

Schedule: M9-M21

OBJECTIVES: Design and implementation of the unified neural network that combines the writing network to model influence, the repetitive neural network to model the evolution of a news story over time and the model of supporting learning to be able to crawl campaign give birth to them.

METHODOLOGY / DESCRIPTION: EU3 will focus on completing a single model of tracking astroturfing campaigns that will combine the production and retransmission of data, the trained model of the underlying network of influence shaped by social media and historical data. In the first step, RNN’s architecture will be designed and tested on historical data of the exhibition and the influence that a news item had (data that PALO has on its customers). The architecture will use EU2 pre-trained GCN forecasts to better predict exposure fluctuations over time (E3.1). Different parameters in the RNN-GCN network will be tested and evaluated, with and without GCN level retraining and the results will be recorded in a relevant report (P3.1). The Deep Reinforcement Learning approach will then be designed to learn from positive astroturfing examples how relevant news is disseminated. This approach will highlight the order in which specific nodes are activated in the social network and what their role is in spreading a false news (E3.2) and the results will be recorded in a relevant deliverable (P3.2). The resulting models will be trained with a lot of data, while the mechanism for the continuous training of the unified model will be developed (E3.3). This process will highlight the ideal configuration of the model in real conditions, and will be imprinted on the final prototype of the model (P3.3). Finally, there will be dissemination and communication actions of the research results that will emerge (E.3.4) in publications and technical reports (P.3.4).

TASKS:

E3.1 Development and evaluation of a single forecasting model (RNN-GCN) in historical data

E3.2 Extension of the model to incorporate the RL technique

E3.3 Model optimization and completion of the final prototype

E3.4 Coordination – Management – Diffusion

DELIVERY:

P3.1 Unified model for predicting the exposure of a news item with time and reports and networks of influence (Report, M21)

P3.2 Combined predictive model that is reinforced (Report, M21)

P3.3 Final Prototype (Prototype, M21)

P3.4 Report on diffusion activities (Report, M21)

Work unit 4

 Title

Completion / Evaluation of platform

Activity Statement

EXPERIMENTAL DEVELOPMENT

Responsible Body

Qixσ

Schedule: M12-M24

OBJECTIVES: The integration of the prototype models in the form of autonomous services in a single platform that can be integrated in the rest of the PALO architecture. The development and integration of EU2 and EU3 models and their pilot evaluation in real conditions.

METHODOLOGY / DESCRIPTION: EU4 will analyze the requirements for the final product, design its architecture and integrate the mechanisms for extracting primary data from the PALO infrastructure and storing the results in it (E4.1). The data interconnection subsystem to be developed (P4.1) will take into account the training data needs arising from EU2 and EU3 and the information generated. The final prototype designed in EU3 will then be incorporated into the original MVP (E4.2) and give the final product (P4.2). The results of the control and experimental tests that will be performed on the platform and its subsystems (E4.3) will be recorded in the final evaluation report of the product in a way that facilitates its business utilization (P.4.3). The whole project (E.4.4) will be reflected in a scientific publication that will summarize the produced research results and the relevant report of dissemination activities for the whole project will be prepared (P.4.4).

TASKS:

E4.1 Development of data interconnection subsystem

E4.2 Integration of forecasting models in the MVP

E4.3 Testing, Testing, Evaluation

E4.4 Coordination – Management – Diffusion

DELIVERY:

P4.1 Data interconnection subsystem (Software, M18)

P4.2 Final platform (Software, M24)

P4.3 Test evaluation results (Report, M24)

P4.4 Report on diffusion activities (Report, M24)