Fighting misinformation in times of coronavirus
Anticipating the future requires a better understanding of the present
This series of videos on social media content explains the needs and opportunities that technology can meet today. At Minsait, an Indra Company, we have developed tools for listening in networks and analyzing trends that help to understand the ever-changing environment. To anticipate the future, we need to understand the present better.
How can we process contents?
Today any communication team faces a common challenge: the volume and diversity of content that is constantly being published on the web. Not only media, but anyone with Internet access can publish content, and the format of this content is diverse: text, links, images, audios, videos... Minsait has built a solution that allows us to process, qualify and prioritize all this volume of content to help these communication teams act as efficiently as possible.
What is our digital listening solution like?
Our digital listening solution responds to different use cases, the characteristics of which will depend on the purpose and recipients of each of them. The use cases are built on a common inCloud technological base, which facilitates scalability and the automatic execution of algorithms, so that analysts can focus on the work of analysis and obtaining useful findings.
What to do when your reputation is at stake?
Another key use case for this type of solution is the listening and detection of content that impacts the reputation of a company, organisation or entity. In this case, not only the detection of content that has a positive or negative impact on reputation is a relevant factor, but also enabling communication and Brand Management teams with mechanisms to measure the level of impact of each conversation, and the success of the risk management actions undertaken by these teams.
What is thematic watch?
A key aspect of thematic watch is the analysis of the most relevant clusters and themes in the conversation.
What metrics do we use?
Once we have identified the most relevant topics in our conversation, we have different metrics that allow us to analyse the clusters in greater depth. This is essential to understand each cluster well and to be able to extract insights that bring value to both us and our client.
How do we transform human language?
In order for algorithms to work on each of the contents that we are able to detect and process, we need to translate them from a "human" language (i.e., texts, tweets, documents...) to a mathematical language (i.e., numbers, vectors, matrices). PLN (Natural Language Processing) techniques are the ones that allow us to perform this translation.
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