After looking in a previous post at some of the technological reasons behind the success of social networks, let us now consider what the new wealth of this social era is, and what opportunities and risks it creates for science.

4 The new wealth: data and the stream

In the scenario described so far, the new wealth consists partly of data and their ownership, and partly of the possibility of sharing them and keeping this continuous flow, the so-called stream, constantly updated. Data ownership can be fairly easily connected to economic wealth: statistical and IT tools for exploring data (data mining) make it possible to extract valuable information that can be used strategically in economic contexts. This action is also promoted at intercontinental level, in the United States and Europe, through initiatives that spread open data policies, meaning policies that encourage opening data to everyone. See the two Italian examples at national and regional level. The data flow, or stream, is equally valuable because it attracts a large number of users: this is what happens on Facebook when users check their wall for friends’ updates. This guarantees a constant user presence, to whom advertising messages can be delivered and who can also be directed toward content of interest, such as advertising or online shopping sites. In this context, we will focus on the aspects most closely related to science.

4.1 Collaborative opportunities for science

Today, the romantic idea of the scientist who, alone in the laboratory, discovers and governs the laws of matter and the cosmos is not only obsolete, but inadequate for facing the challenges that science itself poses.

Science needs teams of scientists working collaboratively to achieve goals. This is required both by the increasing amount of data to analyze, impossible for a single human being without the support of a coordinated research group and appropriate technologies, and by the increasingly interdisciplinary nature of research, which may address the same topic from many different points of view. Think, for example, of DNA, which involves biology, chemistry, theory, mathematics, computation, and more.

These considerations are especially true for those scientific fields, different from laboratory or theoretical studies, that carry out monitoring and are therefore intrinsically linked to social, collaborative, and data-sharing aspects, as in the case of seismology and seismic networks.

Moreover, given how scientific research is now practiced, often through national or European research projects, the usefulness and even the necessity of social and collaborative tools with the characteristics described above is almost obvious. Such tools promote dialogue and information exchange among people working on the same project.

4.2 Communication risks

Social media, however, still present challenges, especially with regard to communication risks, which can be briefly listed:

  • Privacy. The privacy of information, especially sensitive or scientific data, is fundamental and is not always adequately protected. A typical example today is all the websites where it is possible to “log in through Facebook”. Anyone who agrees to this gives the accessed site free access to their data, and the information about this loss of privacy is not always adequately explained.
  • Cryptographic protection: along with the improvement of encryption algorithms, there is also an increase in the resources available to crack protected data. This is a constantly evolving challenge. In a few years, it will probably be possible to discover even long passwords, and the only way to ensure secure access will be to provide more personal data, thereby not adequately protecting one’s privacy.
  • Misunderstanding among people: although SN technologies and, more generally, collaborative technologies promote and simplify communication, cultural, personal, and historical aspects can still complicate the communication process and must not be underestimated. This is evident in large European scientific projects and even more so in pan-European collaborations. From this perspective, collaboration through personal meetings, conferences, and in-person discussions remains the most effective way to think together productively and creatively.
  • The problem of “SPAM”: another major problem to contain is meaningless data, the so-called spam, an IT term used especially for unwanted email. For this reason, metadata associated with data, one of the fundamental elements of the semantic web, are now more important than ever, because they make it possible to give meaning to data and therefore allow a machine to select data of interest from “junk” data simply and quickly.

Conclusions

In conclusion, science in the age of social media can greatly benefit from all the collaborative aspects made available by social networks.

Science will be able to fully express the potential for progress it has always carried if it can free itself from fears linked to the risks of social networks and make the most of their collaborative and social aggregation potential.

**************

Well, this brief excursus on science and social media is over. As usual, any kind of feedback is welcome in the comments below.

See you soon.

HEADER IMAGE Photo by jesse orrico on Unsplash