Visualizing Targeted Audiences
Caption: Social Spread Interface lets people select audiences based on their social connections
We tend to think that the larger our online audience, the more comments and interactions our content will receive. Yet this is not always the case.
The presence of other people can create a diffusion of responsibility. People don't feel as pressured to take action when they feel they share responsibility with others. Paradoxically, when someone poses a question to her entire network, her friends are less likely to respond than if she made the question to a small targeted audience. Directing content to specific groups of people can help users harvest richer online interactions.
Many tech-savvy users use different sharing mechanisms to engage in selective sharing,directing content to specific predefined audiences. These users usually define list of people with particular interests or social ties (e.g., coworkers.)
But, keeping up-to-date lists can be hard and time-consuming. It's also inapplicable for more dynamic interactions. For example, a person who just wrote an article on Gay rights might want the help from their most influential friends in the topic to promote their article.
Another technique involves selecting individuals to target on-the-fly and only sharing the content or message to them. This type of behavior allows for a more dynamic selective sharing experience that is context-driven. This practice is usually referred to as targeted sharing.
Finding the right people at the right time is hard, especially when we are interacting in large communities where it is hard to keep track of everyone's interests and traits. Interactive visualization tools can enable effective audience targeting by prompting a user to learn about their audience and to understand their different interests. To explore these ideas, we designed Hax. Hax is a tool that provides a query interface and multiple visualizations to support users in dynamically choosing audiences for their targeted sharing tasks. We study how users engaged with this tool in the context of sharing and connecting with an audience on Facebook.
Our exploration begins with the three social signals listed below. We briefly define the signal and the reasons for considering it. We decided to begin with these signals as previous work identified they play an important role in targeting audiences:
Shared interests: This signal captures the personal thematic interests of each community member. Many researchers and practitioners view collaborations as a process that aggregates personal interests into collective choices through self-interested bargaining. We believe this bargaining process can be facilitated by making users aware of the personal interests of others, and how they relate to the collaboration task they are promoting.
Location: This signal holds information about the countries, states, and cities where community members live. Collaborations supported by computers have traditionally provided users with the luxury of interacting with others without having to worry about their location. However, location does play an important role when interacting and organizing events within the physical world (e.g., a social rally) as others' spatial-temporal constraints can determine how mucha person will engage in the activity.
Social Spread: This signal holds information about the type of friends and social ties community members have. This signal is important because it can aid members in recognizing prospective newcomers who can help keep the community alive and active.Additionally, the social connections of a member can also help in the spread of the community's messages and visions. Members could thus use this signal to identify the users whose social connectivity would help them the most in distributing certain content.
Caption: Zoomed in Version of our Location Based Interface (top,) Transparent Interface (middle,) and Social Spread (bottom)