When-To-Post on Social Networks

For many users on social networks, one of the goals when broadcasting content is to reach a large audience. The probability of receiving reactions to a message differs for each user and depends on various factors, such as location, daily and weekly behavior patterns and the visibility of the message. While previous work has focused on overall network dynamics and message flow cascades, the problem of recommending personalized posting times has remained an underexplored topic of research. In this study, we formulate a when-to-post problem, where the objective is to find the best times for a user to post on social networks in order to maximize the probability of audience responses. To understand the complexity of the problem, we examine user behavior in terms of post-to-reaction times, and compare cross-network and cross-city weekly reaction behavior for users in different cities, on both Twitter and Facebook. We perform this analysis on over a billion posted messages and observed reactions, and propose multiple approaches for generating personalized posting schedules. We empirically assess these schedules on a sampled user set of 0.5 million active users and more than 25 million messages observed over a 56 day period. We show that users see a reaction gain of up to 17% on Facebook and 4% on Twitter when the recommended posting times are used. We open the dataset used in this study, which includes timestamps for over 144 million posts and over 1.1 billion reactions. The personalized schedules derived here are used in a fully deployed production system to recommend posting times for millions of users every day.Comment: 10 pages, to appear in KDD201

Performance Analysis of Online Social Platforms

We introduce an original mathematical model to analyze the diffusion of posts within a generic online social platform. Each user of such a platform has his own Wall and Newsfeed, as well as his own self-posting and re-posting activity. As a main result, using our developed model, we derive in closed form the probabilities that posts originating from a given user are found on the Wall and Newsfeed of any other. These probabilities are the solution of a linear system of equations. Conditions of existence of the solution are provided, and two ways of solving the system are proposed, one using matrix inversion and another using fixed-point iteration. Comparisons with simulations show the accuracy of our model and its robustness with respect to the modeling assumptions. Hence, this article introduces a novel measure which allows to rank users by their influence on the social platform, by taking into account not only the social graph structure, but also the platform design, user activity (self- and re-posting), as well as competition among posts.Comment: Preliminary version of accepted paper at INFOCOM 2019 (Paris, France

Social Bots: Human-Like by Means of Human Control?

Social bots are currently regarded an influential but also somewhat mysterious factor in public discourse and opinion making. They are considered to be capable of massively distributing propaganda in social and online media and their application is even suspected to be partly responsible for recent election results. Astonishingly, the term `Social Bot' is not well defined and different scientific disciplines use divergent definitions. This work starts with a balanced definition attempt, before providing an overview of how social bots actually work (taking the example of Twitter) and what their current technical limitations are. Despite recent research progress in Deep Learning and Big Data, there are many activities bots cannot handle well. We then discuss how bot capabilities can be extended and controlled by integrating humans into the process and reason that this is currently the most promising way to go in order to realize effective interactions with other humans.Comment: 36 pages, 13 figure

The Double-edged Sword: A Mixed Methods Study of the Interplay between Bipolar Disorder and Technology Use

Human behavior is increasingly reflected or acted out through technology. This is of particular salience when it comes to changes in behavior associated with serious mental illnesses including schizophrenia and bipolar disorder. Early detection is crucial for these conditions but presently very challenging to achieve. Potentially, characteristics of these conditions\u27 traits and symptoms, at both idiosyncratic and collective levels, may be detectable through technology use patterns. In bipolar disorder specifically, initial evidence associates changes in mood with changes in technology-mediated communication patterns. However much less is known about how people with bipolar disorder use technology more generally in their lives, how they view their technology use in relation to their illness, and, perhaps most crucially, the causal relationship (if any exists) between their technology use and their disease. To address these uncertainties, we conducted a survey of people with bipolar disorder (N = 84). Our results indicate that technology use varies markedly with changes in mood and that technology use broadly may have potential as an early warning signal of mood episodes. We also find that technology for many of these participants is a double-edged sword: acting as both a culprit that can trigger or exacerbate symptoms as well as a support mechanism for recovery. These findings have implications for the design of both early warning systems and technology-mediated interventions

User Experiences of Regret While Engaging with Social Media

Social media offers users the ability to participate with a social network of others in a process of sharing and fellowship, presenting an impression of self and the ability to monitor constructed expressions. Recent studies examining the ritual view of communication, impression management, self-regulation, and self-reflective capabilities show each of these plays a role when using certain social media sites. However, a research gap exists regarding the use of any social media and the perspectives of young adult users during the scenario of experiencing regret as the result of engaging with social media. The study is a mixed-methods exploratory study analyzing emergent themes of this phenomenon. A survey of qualitative open-ended questions and quantitative directed-response choices was administered to 332 individuals. Descriptive, In-Vivo, Emotion and Pattern qualitative coding methods were administered for detailed analysis, as well as SPSS frequency analysis to those reporting the experience of regret (n = 152) while using social media. Findings reveal that users engage in a ritual view of communication while using social media that may be influenced positively or negatively by content posted or the frequency of use. Users seek to manage their own personal impressions to others, while also affecting other users\u27 impressions within the mediated network Self-regulation was in force, suspended or altered during the regrettable social media post, yet self-reflective capabilities assisted user comprehension of regret and post ramifications. Action regrets took place with both hot and cold emotional states. Frequency of social media posting decreased after experiencing instances of regrettable posts