InnoGraph analyses specific innovation trends through info graphics based on stories that were shared by experts on WAI Social Networks.
In a volume and revenue driven environment, clarifying sense and identifying biases behind the use of communication tools seems critical to solve identified issues. How do innovation experts create substantial content that is consistent enough across technologies to inspire the right actions from their communities?
The need for substantial content
Depending on social and historical contexts, wording conveys different meanings to specific audiences. Over time, strategic words and concepts may therefore lose their substance. In below example covering the word “populism” in French, Gérard Mauger concludes “Nowadays, as it was already the case in the past, two opposite representations of populism confront each other: one’s social racism is used to denounce others’ populism.”
De sorte que, aujourd’hui comme hier, deux représentations diamétralement opposées du populaire s’affrontent : le racisme de classe des uns sert à dénoncer le populisme des autres.
Lire plus: “Populisme, itinéraire d’un mot voyageur”, Gerard Mauger, Le Monde Diplomatique
Multiple channels exist to tailor messages with a defined format and tone for specific audiences and user experiences. Those constraints represent an additional influence to the overall content creation process, including timing and localization (language, platforms abilities and publication tools offered, audience reach).
In fact, content marketing is more than just the process of creating content – it’s also the process of sharing that content across the web, and ensuring that the content falls in front of the eyes of the right types of internet user at the right time.
Read more: “What is Content Marketing and how will my business profit”, John Waldron, Mark IT Write
There is a need to understand the background of shared conversations. It provides insights and better characterizes the issues at stake. A language analysis that includes context is a critical component of making sense of problems and related solutions.
But linguistic analysis of this sort misses a critical component: context. Is the word being used by an individual, publication or company – or one million individuals and companies? What else was said around it? Language analysis that also includes context can not only clear up confusion and add insight but can also be used to help solve particularly thorny global problems.
Read more: “In a data obsessed era, language matters. Here’s why.“, Bob Goodson, World Economic Forum
The technological advances brought by AI drive a number of systemic challenges: in the following article, “Infosys reveals that a quarter of young people aged 16 to 25 years old thinks their work will be performed by machines in the next ten years.”
Par exemple, McKinsey estime que 45% des emplois actuels pourraient être automatisés en utilisant des technologies déjà existantes. A son tour, Forrester prédit que plus de 9 millions d’emplois auront disparus d’ici 10 ans sous l’effet de la fameuse IA. Les futurs professionnels sont d’ailleurs lucides : une enquête Infosys révèle que plus d’un quart des jeunes de 16 à 25 ans pense que leur travail sera réalisé d’ici 10 ans par des machines.
Part of this impact is driven by the creative potential specialists wish to explore. Technologies such as Artificial Intelligence are being used to produce creative content. In below example, AI was able to write a movie scenario.
Except Sunspring isn’t the product of Hollywood hacks—it was written entirely by an AI. To be specific, it was authored by a recurrent neural network called long short-term memory, or LSTM for short.
Read more: “Movie written by algorithm turns out to be hilarious and intense“, Annalee Newitz, Arstechnica
On top of creativity, machines are given the ability to learn from the data they receive, providing a certain degree of autonomy in their development, as a way to accelerate and optimize their efficiency.
But machine-learning algorithms are different: they figure it out on their own, by making inferences from data. And the more data they have, the better they get. Now we don’t have to programme computers; they programme themselves.
Read more: “Why you need to understand Machine Learning“, Pedro Domingos, World Economic Forum
From knowledge to change
When used with the appropriate role and question, data analytics tools can contribute to clarify human and technological sense. In below example, Alison Smit explains what tools she uses to create streams of relevant content across social media conversations.
Some of it is relevant to you, and some of it is not. What’s relevant really depends on the question you need to answer. To trim social media conversations down to just the relevant ones, create topics. Each topic (you need one, but you could create dozens) is a list of keywords that must appear in a post in social media for that post to be added to your dataset. Basically, you’re creating a custom subset of social data that contains only relevant content.
Read more: “How to make social data more relevant, trustworthy and valuable“, Alison Smith, IBM
There certainly is a commercial benefit to develop substantial content optimized by social analytics tools and applications. This is the reason why marketing strategies and developments are strongly adapted to the ever changing environment of social media analysis.
Brands are often the most affected by significant changes to social networks. Important updates often enhance user experience and lean towards showing content that from friends and family, rather than ads or branded content.
“The Psychology of Change: Why we react when social networks shit”, Ash Read, The Next Web
Other benefits could be defined though. In worst cases, those benefits could serve issues and goals that damage links within communities. In best cases, they could optimize the way we communicate and accelerate problem solving, simply by developing the appropriate content.
Positive words can’t completely solve a problem. They certainly can, however, help a message be better received. Affirmative words introduce acceptance easier and allow employees to be in better mindsets after disagreements.
“Knowledge barrier: How words may impede problem-solving?”, Stefan Swanepoel, Entrepreneur
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Photograph: Nirina Photography