Search

How might computing support us in becoming our better, more emotionally resilient selves? We explore this in an interview with the team from Microsoft Research's Human Understanding and Empathy group.

Social media and networking websites, such as Twitter and Facebook, generate large quantities of information and have become mechanisms for real-time content dissipation to users. An important question that arises is: how do we sample such social media information spaces in order to deliver relevant content on a topic to end users? Notice that these large-scale information spaces are inherently diverse, featuring a wide array of attributes such as location, recency, degree of diffusion effects in the network and so on. Naturally, for the end user, different levels of diversity in social media content can significantly impact the information consumption experience: low diversity can provide focused content that may be simpler to understand, while high diversity can increase breadth in the exposure to multiple opinions and perspectives. Hence to address our research question, we turn to diversity as a core concept in our proposed sampling methodology. Here we are motivated by ideas in the "compressive sensing" literature and utilize the notion of sparsity in social media information to represent such large spaces via a small number of basis components. Thereafter we use a greedy iterative clustering technique on this transformed space to construct samples matching a desired level of diversity. Based on Twitter Firehose data, we demonstrate quantitatively that our method is robust, and performs better than other baseline techniques over a variety of trending topics. In a user study, we further show that users find samples generated by our method to be more interesting and subjectively engaging compared to techniques inspired by state-of-the-art systems, with improvements in the range of 15--45%.

The ability to regulate our emotions plays an important role in our psychological and physical health. Regulating emotions influences how and when emotions are expressed. We performed a large scale, longitudinal observational study to investigate the effect of emotion regulation ability on expressed affect. We found that expression of negative affect increased throughout the day. For people who suppress emotion this increase is slower that for those who do not. For those with stronger cognitive reappraisal abilities, though not significant, there was a trend for higher positive affect and negative affect increased significantly less steeply, suggesting that they might experience more positive and less negative affect. These results reflect some of the first results based on large scale, continuous tracking of behavioral expression of emotion longitudinally. Our results demonstrate the need to carefully consider the time of day and emotion regulation ability, in addition to gender and age, when attempting to automatically infer affective states for facial behavior.

The Future Conversations workshop at CHIIR'21 looked to the future of search, recommendation, and information interaction to ask: where are the opportunities for conversational interactions? What do we need to do to get there? Furthermore, who stands to benefit? The workshop was hands-on and interactive. Rather than a series of technical talks, we solicited position statements on opportunities, problems, and solutions in conversational search in all modalities (written, spoken, or multimodal). This paper -co-authored by the organisers and participants of the workshop- summarises the submitted statements and the discussions we had during the two sessions of the workshop. Statements discussed during the workshop are available at https://bit.ly/FutureConversations2021Statements.

In the past year, have you found yourself under stress? Have you ever wished for help coping? Imagine if, throughout the pandemic, you'd had a virtual therapist powered by an artificial intelligence (AI) system, an entity that empathized with you and gradually got to know your moods and behaviors. Therapy is just one area where we think an AI system that can recognize and interpret emotions could offer great benefits to people.