Algorithms, applications and systems towards interpretable pattern mining from multi-aspect data

How do humans move around in the urban space and how do they differ when the city undergoes terrorist attacks? How do users behave in Massive Open Online courses~(MOOCs) and how do they differ if some of them achieve certificates while some of them not? What areas in the court elite players, such as Stephen Curry, LeBron James, like to make their shots in the course of the game? How can we uncover the hidden habits that govern our online purchases? Are there unspoken agendas in how different states pass legislation of certain kinds? At the heart of these seemingly unconnected puzzles is this same mystery of multi-aspect mining, i.g., how can we mine and interpret the hidden pattern from a dataset that simultaneously reveals the associations, or changes of the associations, among various aspects of the data (e.g., a shot could be described with three aspects, player, time of the game, and area in the court)? Solving this problem could open gates to a deep understanding of underlying mechanisms for many real-world phenomena. While much of the research in multi-aspect mining contribute broad scope of innovations in the mining part, interpretation of patterns from the perspective of users (or domain experts) is often overlooked. Questions like what do they require for patterns, how good are the patterns, or how to read them, have barely been addressed. Without efficient and effective ways of involving users in the process of multi-aspect mining, the results are likely to lead to something difficult for them to comprehend. This dissertation proposes the M^3 framework, which consists of multiplex pattern discovery, multifaceted pattern evaluation, and multipurpose pattern presentation, to tackle the challenges of multi-aspect pattern discovery. Based on this framework, we develop algorithms, applications, and analytic systems to enable interpretable pattern discovery from multi-aspect data. Following the concept of meaningful multiplex pattern discovery, we propose PairFac to close the gap between human information needs and naive mining optimization. We demonstrate its effectiveness in the context of impact discovery in the aftermath of urban disasters. We develop iDisc to target the crossing of multiplex pattern discovery with multifaceted pattern evaluation. iDisc meets the specific information need in understanding multi-level, contrastive behavior patterns. As an example, we use iDisc to predict student performance outcomes in Massive Open Online Courses given users' latent behaviors. FacIt is an interactive visual analytic system that sits at the intersection of all three components and enables for interpretable, fine-tunable, and scrutinizable pattern discovery from multi-aspect data. We demonstrate each work's significance and implications in its respective problem context. As a whole, this series of studies is an effort to instantiate the M^3 framework and push the field of multi-aspect mining towards a more human-centric process in real-world applications

Twitter in Academic Conferences: Usage, Networking and Participation over Time

Twitter is often referred to as a backchannel for conferences. While the main conference takes place in a physical setting, attendees and virtual attendees socialize, introduce new ideas or broadcast information by microblogging on Twitter. In this paper we analyze the scholars' Twitter use in 16 Computer Science conferences over a timespan of five years. Our primary finding is that over the years there are increasing differences with respect to conversation use and information use in Twitter. We studied the interaction network between users to understand whether assumptions about the structure of the conversations hold over time and between different types of interactions, such as retweets, replies, and mentions. While `people come and people go', we want to understand what keeps people stay with the conference on Twitter. By casting the problem to a classification task, we find different factors that contribute to the continuing participation of users to the online Twitter conference activity. These results have implications for research communities to implement strategies for continuous and active participation among members

Mispositioned Neurokinin-1 Receptor-Expressing Neurons Underlie Heat Hyperalgesia in Disabled-1 Mutant Mice.

Reelin (Reln) and Disabled-1 (Dab1) participate in the Reln-signaling pathway and when either is deleted, mutant mice have the same spinally mediated behavioral abnormalities, increased sensitivity to noxious heat and a profound loss in mechanical sensitivity. Both Reln and Dab1 are highly expressed in dorsal horn areas that receive and convey nociceptive information, Laminae I-II, lateral Lamina V, and the lateral spinal nucleus (LSN). Lamina I contains both projection neurons and interneurons that express Neurokinin-1 receptors (NK1Rs) and they transmit information about noxious heat both within the dorsal horn and to the brain. Here, we ask whether the increased heat nociception in Reln and dab1 mutants is due to incorrectly positioned dorsal horn neurons that express NK1Rs. We found more NK1R-expressing neurons in Reln-/- and dab1-/- Laminae I-II than in their respective wild-type mice, and some NK1R neurons co-expressed Dab1 and the transcription factor Lmx1b, confirming their excitatory phenotype. Importantly, heat stimulation in dab1-/- mice induced Fos in incorrectly positioned NK1R neurons in Laminae I-II. Next, we asked whether these ectopically placed and noxious-heat responsive NK1R neurons participated in pain behavior. Ablation of the superficial NK1Rs with an intrathecal injection of a substance P analog conjugated to the toxin saporin (SSP-SAP) eliminated the thermal hypersensitivity of dab1-/- mice, without altering their mechanical insensitivity. These results suggest that ectopically positioned NK1R-expressing neurons underlie the heat hyperalgesia of Reelin-signaling pathway mutants, but do not contribute to their profound mechanical insensitivity

Tweeting Questions in Academic Conferences: Seeking or Promoting Information?

The fast growth of social media has reshaped the traditional way of human interaction and information seeking behavior, which draws research attention on characterizing the new information seeking paradigm. However, results from previous studies might not be well grounded under certain social settings. In this paper, we leverage machine learning techniques to identify different types of question tweets within academic communities as an example of one particular social context. By studying over 160 thousands of tweets posted by 30 academic communities, we discovered a different landscape of information-seeking behaviors, where less tweets are regarded as question tweets, and more real information-seeking tweets are observed. We also found that users respond differently with different types of question tweets. We believe our study would be beneficial for understanding the information seeking behaviors in social media.ye