Who are Like-minded: Mining User Interest Similarity in Online Social Networks

In this paper, we mine and learn to predict how similar a pair of users' interests towards videos are, based on demographic (age, gender and location) and social (friendship, interaction and group membership) information of these users. We use the video access patterns of active users as ground truth (a form of benchmark). We adopt tag-based user profiling to establish this ground truth, and justify why it is used instead of video-based methods, or many latent topic models such as LDA and Collaborative Filtering approaches. We then show the effectiveness of the different demographic and social features, and their combinations and derivatives, in predicting user interest similarity, based on different machine-learning methods for combining multiple features. We propose a hybrid tree-encoded linear model for combining the features, and show that it out-performs other linear and treebased models. Our methods can be used to predict user interest similarity when the ground-truth is not available, e.g. for new users, or inactive users whose interests may have changed from old access data, and is useful for video recommendation. Our study is based on a rich dataset from Tencent, a popular service provider of social networks, video services, and various other services in China

First impressions: A survey on vision-based apparent personality trait analysis

© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Personality analysis has been widely studied in psychology, neuropsychology, and signal processing fields, among others. From the past few years, it also became an attractive research area in visual computing. From the computational point of view, by far speech and text have been the most considered cues of information for analyzing personality. However, recently there has been an increasing interest from the computer vision community in analyzing personality from visual data. Recent computer vision approaches are able to accurately analyze human faces, body postures and behaviors, and use these information to infer apparent personality traits. Because of the overwhelming research interest in this topic, and of the potential impact that this sort of methods could have in society, we present in this paper an up-to-date review of existing vision-based approaches for apparent personality trait recognition. We describe seminal and cutting edge works on the subject, discussing and comparing their distinctive features and limitations. Future venues of research in the field are identified and discussed. Furthermore, aspects on the subjectivity in data labeling/evaluation, as well as current datasets and challenges organized to push the research on the field are reviewed.Peer ReviewedPostprint (author's final draft

Sexual risk reduction interventions for patients attending sexual health clinics: a mixed-methods feasibility study

Background: Sexually transmitted infections (STIs) continue to represent a major public health challenge. There is evidence that behavioural interventions to reduce risky sexual behaviours can reduce STI rates in patients attending sexual health (SH) services. However, it is not known if these interventions are effective when implemented at scale in SH settings in England. Objectives: The study had two main objectives: 1. develop and pilot a package of evidence-based sexual risk reduction interventions that can be delivered through SH services; 2. assess the feasibility of conducting a randomised controlled trial (RCT) to determine effectiveness against usual care. Design: The project was a multi-stage mixed methods study, with developmental and pilot RCT phases. Preparatory work included a systematic review; analysis of national surveillance data, and development of a triage algorithm; interviews and surveys with SH staff and patients to identify, select and adapt interventions. A pilot cluster RCT was planned for eight SH clinics; the intervention would be offered in four clinics, with qualitative and process evaluation to assess feasibility and acceptability. Four clinics acted as controls; in all clinics, participants would be consented to a 6-week follow-up STI screen. Setting: SH clinics in England. Participants: Young people (aged 16-25 years old) and men who have sex with men. Intervention: A three-part intervention package: 1. triage tool to score patients as high or low risk of STI infection using routine data; 2. a study-designed webpage with tailored sexual health information for all patients, regardless of risk; 3. a brief one-to-one session based on motivational interviewing for high risk patients. Main outcome measures: The three outcomes were: acceptability of the intervention to patients and SH providers; feasibility of delivering the interventions within existing resources; and feasibility of obtaining follow-up data on STI diagnoses (primary outcome in a full trial). Results: We identified 33 relevant trials from the systematic review, including: videos, peer support, digital, and brief one-to-one sessions. Patients and SH providers showed preferences for one-to-one and digital interventions, and providers indicated these intervention types could feasibly be implemented in their settings. There were no appropriate digital interventions that could be adapted in time for the pilot; therefore, we created a placeholder for the purposes of the pilot. The intervention package was piloted in two SH settings, rather than the planned four. Several barriers were found to intervention implementation, including a lack of trained staff time and clinic space. The intervention package was theoretically acceptable, but we observed poor engagement. We recruited patients from six clinics for the follow-up, rather than eight. The completion rate for follow-up was lower than anticipated (16% versus 46%). Limitations: Fewer clinics were included in the pilot than planned limiting the ability to make strong conclusions on RCT feasibility. Conclusion: We were unable to conclude whether a definitive RCT would be feasible due to challenges in implementation of a pilot, but have laid the groundwork for future research in the area

Advertising Content and Consumer Engagement on Social Media: Evidence from Facebook

We describe the effect of social media advertising content on customer engagement using data from Facebook. We content-code 106,316 Facebook messages across 782 companies, using a combination of Amazon Mechanical Turk and natural language processing algorithms. We use this data set to study the association of various kinds of social media marketing content with user engagement—defined as Likes, comments, shares, and click-throughs—with the messages. We find that inclusion of widely used content related to brand personality—like humor and emotion—is associated with higher levels of consumer engagement (Likes, comments, shares) with a message. We find that directly informative content—like mentions of price and deals—is associated with lower levels of engagement when included in messages in isolation, but higher engagement levels when provided in combination with brand personality–related attributes. Also, certain directly informative content, such as deals and promotions, drive consumers’ path to conversion (click-throughs). These results persist after incorporating corrections for the nonrandom targeting of Facebook’s EdgeRank (News Feed) algorithm and so reflect more closely user reaction to content than Facebook’s behavioral targeting. Our results suggest that there are benefits to content engineering that combines informative characteristics that help in obtaining immediate leads (via improved click-throughs) with brand personality–related content that helps in maintaining future reach and branding on the social media site (via improved engagement). These results inform content design strategies. Separately, the methodology we apply to content-code text is useful for future studies utilizing unstructured data such as advertising content or product reviews

State of the art 2015: a literature review of social media intelligence capabilities for counter-terrorism

Overview This paper is a review of how information and insight can be drawn from open social media sources. It focuses on the specific research techniques that have emerged, the capabilities they provide, the possible insights they offer, and the ethical and legal questions they raise. These techniques are considered relevant and valuable in so far as they can help to maintain public safety by preventing terrorism, preparing for it, protecting the public from it and pursuing its perpetrators. The report also considers how far this can be achieved against the backdrop of radically changing technology and public attitudes towards surveillance. This is an updated version of a 2013 report paper on the same subject, State of the Art. Since 2013, there have been significant changes in social media, how it is used by terrorist groups, and the methods being developed to make sense of it.  The paper is structured as follows: Part 1 is an overview of social media use, focused on how it is used by groups of interest to those involved in counter-terrorism. This includes new sections on trends of social media platforms; and a new section on Islamic State (IS). Part 2 provides an introduction to the key approaches of social media intelligence (henceforth ‘SOCMINT’) for counter-terrorism. Part 3 sets out a series of SOCMINT techniques. For each technique a series of capabilities and insights are considered, the validity and reliability of the method is considered, and how they might be applied to counter-terrorism work explored. Part 4 outlines a number of important legal, ethical and practical considerations when undertaking SOCMINT work