Crowd Learning and Crowdsourcing: Trends in Contemporary Journalism

In the last few years, technological advancements have reshaped the way every individual, society, and industry functions. A variety of terminology is currently used in regard to crowds, e.g. crowdsourcing, collective intelligence, citizen science, and crowd learning. In times of digitalization, we need more than ever new approaches to optimize the training and professional competence of a journalist. The important case that we have to remember is that our behaviour creates data. This data can be effectively used to learn from crowds.

Its very premise has been leveraged by modern technological tools to establish an effective connection with people from around the world. Through this approach of collaborative learning, one does not just gain academic knowledge on an individual level, but also nurtures his social skills and helps empower himself and others. Such a highly beneficial method of crowd learning helps one develop holistically.

Here’s how:

• Development of cognitive skills and creativity. When an individual seeks a solution in a peer-to-peer environment, they tend to get alternate explanations from various perspectives. This does not just enrich their knowledge about a certain topic but also leads to the development of logical reasoning capabilities and broadened perspective.

  Furthermore, active engagement and discussion of ideas and concepts in a supportive atmosphere result in an increased appetite and curiosity for knowledge. Leaners begin to probe deeper, observe, question, analyze, and explore with fellows in a crowd learning structure. The variety of solutions to a question asked also leads to recapitulation and better retention. Moreover, this dynamic environment leads to an increase in productivity as learners with varying knowledge collectively brainstorm to seek solutions.

• Enrichment of socio-personal skills. Over the years, crowd learning has proved to be a boon in honing of socio-personal skills. With reduced social interactions and more time spent in interaction with devices rather than people, socio-personal skills are at an all-time low among youngsters. In such a scenario, when individuals from different backgrounds and levels of expertise come together to collectively exchange knowledge, ideas, and experiences, it helps inculcate respect and harmony for diverse cultures.

  Furthermore, this method serves as an effective platform for developing social skills as students actively interact with each other to seek and suggest solutions and concepts.

• Better self-management. It may be tough to keep up with the knowledge imparted when learning in a conventional environment. This is because every individual possesses unique comprehension and grasping capabilities. Crowd learning, therefore, helps develop strong self-management skills as students get to learn according to their pace preference, and learning style.

  Furthermore, by deeming an individual accountable for their own progress in a collaborative environment, this approach also encourages responsibility for learning. Equipped with a substantial knowledge base and utmost support from fellow learners, crowd learning develops confidence in an individual to face challenging situations, academically and otherwise. It has a strong connotation with crowdsourcing– the combination of “wisdom of the crowd” and “outsourcing”.

Social Impact, Social Change, and Peer Learning

In journalism, crowdsourcing is usually the act of specifically inviting a group of people to participate in a reporting task — such as newsgathering, data collection, or analysis — through an open call for input; personal experiences; documents; or other contributions. One of the perspectives of using crowdsourcing in journalism is to view and use derivative information as a valuable part of the information ecosystem that can be treated as meta-data, or as the information that might be able to provide a basis for navigating the information space.

Each event affects a large number of people that causes disruption to normal social routines. The interaction of crowdsourcing with investigative journalism can lead to the deeper study of the social, technical, and informational concerns of large-scale emergency response; it considers the interactions and concerns of formal responders as well as members of the public.

Thus, journalism crowdsourcing may be of great help when analyzing examples of mass disruption events such as natural disasters, acts of terrorism, mass emergencies, extreme weather events, and political protests.

Millions of people turn to social media platforms to participate in a wide range of information activities, e.g. share information, seek information about the status of people or property gather and synthesize information, seek or offer assistance, and coordinate action. These activities represent a digital-age equivalent to the informational convergence behavior long known to occur in the wake of disaster.

For instance, Twitter has consistently been appropriated for use during mass disruption events by those affected, digital volunteers, and emergency response organizations. Its appeal comes from its short message, broadcast, and public nature: most posts can be seen by anyone, which means that interactions are not “walled” away to a restricted group.

In the context of political disruptions, the value of locating information is less obvious. Social media may be used most effectively by pro-government forces in their efforts to crush opposition protests, even putting protestors in more danger by allowing them to be more easily identified. Though accepting some risk in identifying on-the-ground participants in this way, this research contends that the great majority of Twitter users during mass disruption events, even political protests, understand that their communications are public and want their accounts to be identified with the protest and their voices to be heard.

Moreover, the social media community can collaboratively act to identify bad information. For instance, tweets containing false information were more likely to be challenged by other Twitterers. Social context, which consists of social interactions within social media (friend relationships, group membership, lists), can work as a collaborative filter to identify the value of information. Twitterers use the retweet as a recommendation mechanism during crisis events.

Certain characteristics of crowd behavior could act as a collaborative filter for identifying people tweeting from the ground during a mass disruption event.

To present “the facts” and the “truth about the facts,” journalists and storytellers need to practice a discipline of verification.

Here are six different calls to action to verify the information:

• Voting — prioritizing which stories reporters should tackle.
• Witnessing — sharing what you saw during a news event.
• Sharing personal experiences — telling what you know about your life experience.
• Tapping specialized expertise — contributing data or unique knowledge.
• Completing a task — volunteering time or skills to help create a news story.
• Engaging audiences — joining in call-outs that can range from informative to playful.

The “sharing of personal experiences” also means giving credit where it’s due and verifying original sources of information. Due to reaps of content available online, especially on platforms like Twitter, and deadline-driven stories that need to get published – research time is often limited. To help with this publishers are turning to software solutions to sift through the clutter and determine what is valid, and what is not.

Data Machine learning algorithms are a form of artificial intelligence that can be used for a variety of applications, including natural language processing, search engine algorithms, spam detection software, and collaborative filters.

Machine learning algorithms have been employed by researchers to classify Twitter messages and profiles. For example, in 2011 such classifiers were used to identify tweets sent during crisis events that contain situational awareness information and tested the viability of using a machine learning classifier to determine the location of general Twitter profiles—not related to a specific event or another context— by examining the implicit location information contained within tweet text. For mass disruption events, location identification may need to achieve a finer level of granularity, i.e. by city, neighborhood, or even city block.

When practicing crowdsourcing, journalists’ and storytellers’ jobs can be made easier with things like software and external platforms. Publishers who turn to these solutions for crowdsourcing are not only getting it right but getting it right faster for quicker turnaround times on breaking news stories.

An example of software used for crowdsourcing includes Elvis Digital Asset Management (DAM). When integrated with things like Twitter and artificial intelligence and artificial intelligence (AI), images for breaking news stories can be sources faster by:

• Eliminating irrelevant content.

• Verifying images quickly.

• Citing sources with automated links to Twitter.

• See how it’s done here.

Media organizations use the platform to securely accept documents from and communicate with, anonymous sources. These solutions, along with objective storytelling and ethical journalism, can help with, engagement and storytelling.

With the rise of the internet correlating with the rise of crowdsourcing and crowd learning, technologies have made good journalism easier. Identifying sources, getting the right research, and following breaking-news developments as it happens provides varied angles that weren’t available in pre-social media and the internet. Right technology tools and software solutions, give easier access to publishers to gather, assess, create, and present news and information to readers in more efficient, proven ways.

Also published here.