  Current language processing technologies allow the creation of conversational
chatbot platforms. Even though artificial intelligence is still too immature to
support satisfactory user experience in many mass market domains,
conversational interfaces have found their way into ad hoc applications such as
call centres and online shopping assistants. However, they have not been
applied so far to social inclusion of elderly people, who are particularly
vulnerable to the digital divide. Many of them relieve their loneliness with
traditional media such as TV and radio, which are known to create a feeling of
companionship. In this paper we present the EBER chatbot, designed to reduce
the digital gap for the elderly. EBER reads news in the background and adapts
its responses to the user's mood. Its novelty lies in the concept of
"intelligent radio", according to which, instead of simplifying a digital
information system to make it accessible to the elderly, a traditional channel
they find familiar -- background news -- is augmented with interactions via
voice dialogues. We make it possible by combining Artificial Intelligence
Modelling Language, automatic Natural Language Generation and Sentiment
Analysis. The system allows accessing digital content of interest by combining
words extracted from user answers to chatbot questions with keywords extracted
from the news items. This approach permits defining metrics of the abstraction
capabilities of the users depending on a spatial representation of the word
space. To prove the suitability of the proposed solution we present results of
real experiments conducted with elderly people that provided valuable insights.
Our approach was considered satisfactory during the tests and improved the
information search capabilities of the participants.
