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.) They then post content contextualized so that it is relevant to the interests of the people in each list.
But, keeping up-to-date lists can be hard and time-consuming. It's also inapplicable for more dynamic interactions, based on location, or popularity of the targeted users. For example, someone organizing a social rally might only want to target the friends who are in town on a particular day. Or 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. Previous work, including Facebook's graph search, used list-based interfaces to recommend people with a certain expertise, interest or trait. But these systems do not let people easily explore and compare the different characteristics of the recommended individuals. However, these characteristics can play an important role when people are deciding whether or not to select a person for a particular collaboration or interaction. People want ways through which they can understand the space of users they can target to interact. List based interfaces don't let people have quick overviews of their possible targeted audience. Nor do they let people easily zoom in and compare specific users. But to have rich sharing experiences people need to understand their audience: an overview and its details. For instance, a person wanting to post on LGBT might need to understand that half of her interested audience is from Russia. This might mean that to better engage with them, she should include some LGBT issues happening in their country.
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.
We believe the data modeling techniques that work for content categorization and information retrieval can be adapted to mine people's interests and retrieve audiences relevant to users' diverse needs. But, while specialized data modeling algorithms exist that can correctly categorize data, they rarely fully capture the complex and ever-changing decision-making process for targeting an audience. We therefore opt to integrate data visualizations that incorporate a human-in-the-loop approach. We designed different data visualizations that highlight specific traits, or social signals, of relevant individuals in order to aid users in their audience targeting tasks.
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.
For more, see our full paper to be presented at COOP2014, Visualizing Targeted Audiences co-authored by Saiph Savage, Angus Forbes, Carlos Toxtli, Grant McKenzie, and Shloka Desai
