Understanding the behaviour and influence of automated social agents

Soft-bound submitted: Fri 23 Feb 2018 Corrections submitted: Mon 30 Jul 2018 Corrections approved: Tue 7 Aug 2018 Apollo submitted: Wed 22 Aug 2018 Hard-bound submitted: Fri 24 Aug 2018Online social networks (OSNs) have seen a remarkable rise in the presence of automated social agents, or social bots. Social bots are the new computing viral, that are surreptitious and clever. What facilitates the creation of social agents is the massive human user-base and business-supportive operating model of social networks. These automated agents are injected by agencies, brands, individuals, and corporations to serve their work and purpose; utilising them for news and emergency communication, marketing, social activism, political campaigning, and even spam and spreading malicious content. Their influence was recently substantiated by coordinated social hacking and computational political propaganda. The thesis of my dissertation argues that automated agents exercise a profound impact on OSNs that transforms into an array of influence on our society and systems. However, latent or veiled, these agents can be successfully detected through measurement, feature extraction and finely tuned supervised learning models. The various types of automated agents can be further unravelled through unsupervised machine learning and natural language processing, to formally inform the populace of their existence and impact.Sep'14-Aug'17, Marie Curie ITN METRICS, Early-Stage Researcher Sep'17, UMobile, Research Associate Oct'17-Mar'18, EPSRC Global Challenges Research Fund, Research Associat

Characterizing the role of bots’ in polarized stance on social media

There is a rising concern with social bots that imitate humans and manipulate opinions on social media. Current studies on assessing the overall effect of bots on social media users mainly focus on evaluating the diffusion of discussions on social networks by bots. Yet, these studies do not confirm the relationship between bots and users’ stances. This study fills in the gap by analyzing if these bots are part of the signals that formulated social media users’ stances towards controversial topics. We analyze users’ online interactions that are predictive to their stances and identify the bots within these interactions. We applied our analysis on a dataset of more than 4000 Twitter users who expressed a stance on seven different topics. We analyzed those users’ direct interactions and indirect exposures with more than 19 million accounts. We identify the bot accounts for supporting/against stances, and compare them to other types of accounts, such as the accounts of influential and famous users. Our analysis showed that bot interactions with users who had specific stances were minimal when compared to the influential accounts. Nevertheless, we found that the presence of bots was still connected to users’ stances, especially in an indirect manner, as users are exposed to the content of the bots they follow, rather than by directly interacting with them by retweeting, mentioning, or replying

Agents for Fighting Misinformation Spread on Twitter: Design Challenges

Containing misinformation spread on social media has been acknowledged as a great socio-technical challenge in the last years. Despite advances, practical and timely solutions to properly communicate verified (mis)information to social media users are an evidenced need. We introduce a multi-agent approach to bridge Twitter users with fact-checked information. First, a social bot, which nudges users sharing verified misinformation, and a conversational agent that verifies if there is a reputable fact-check available and explains existing assessments in natural language. Both agents share the same requirements of evoking trust and being perceived by Twitter users as an opportunity to build their media literacy. To this end, two preliminary human-centred studies are presented, the first one looking for an adequate identity for the bot, and the second for understanding preferences for credibility indicators when explaining the assessment of misinformation. The results indicate what this design research should pursue to create agents that are consistent in their presentation, friendly, engaging, and credible

Stance characterization and detection on social media

Stance detection refers to the task of identifying a viewpoint as either supporting or opposing a given topic. The current research on socio-political opinion mining on social media is still in its infancy. Most computational approaches in this field are limited to the independent use of textual elements of a user’s posts from social factors such as homophily and network structure. This thesis provides a thorough study of stance detection on social media and assesses various online signals to identify the stance and understand its association with the analysed topic. We explore the task of detecting stance on Twitter, which is a well-known social media platform where people often express stance implicitly or explicitly. First, we examine the relation between sentiment and stance and analyse the inter-play between sentiment polarity and expressed stance. For this purpose, we extend the current SemEval stance dataset by annotating tweets related to four new topics with sentiment and stance labels. Then, we evaluate the effectiveness of sentiment analysis methods on stance prediction using two stance datasets. Second, we examine the multi-modal representation of stance on social media by evaluating multiple stance detection models using textual content and online interactions. The finding of this chapter suggests that using social interactions along with other textual features can improve the stance detection model. Moreover, we show how an unconscious social interaction can reveal the stance. Next, we design an online framework to preserve users’ privacy concerning the implicitly inferred stance on social media. Thus, we evaluate the effectiveness of the two stance obfuscation methods and use different stance detection models to measure the overall performance of the proposed framework. Finally, we study the dynamics of polarized stance to understand the factors that influence online stance. Particularly, we extend the analysis of online stance signals and examine the interplay between stance and automated accounts (bots). Furthermore, we pose the problem of gauging the bots’ effect on polarized stance through a sole focus on the diffusion of bots on the online social network

Chatbots for Modelling, Modelling of Chatbots

Tesis Doctoral inédita leída en la Universidad Autónoma de Madrid, Escuela Politécnica Superior, Departamento de Ingeniería Informática. Fecha de Lectura: 28-03-202

Automated Synthesis of Chatbots for Configuring Software Product Lines

Software product lines are a method for creating a family of products that share a typical managed set of features, satisfy the precise needs of a selected domain, and provide an improved quality of software systems by systematically reusing software artefacts at reduced cost and time. A feature model represents the space of all possible and allowed configurations of all products in an SPL. Various predefined feature combinations enable the product to be personalized based on specific user requirements. However, because some features are interdependent and the feature models may have many options, users must understand the implications of selecting the correct feature combinations for the product derivation. Chatbot support can address this challenge by guiding the user through a suitable set of features for the product configuration process. Users can interact with a chatbot using natural language in a familiar environment like Telegram, Slack, or Facebook. In this work, we propose chatbots in the configuration of software product lines based on feature models and present SPLBOT, an approach for SPLs chatbot generators. The methodology relies on Eclipse, FeatureIDE, and CONGA (for Dialogflow chatbot generation). Furthermore, we present an evaluation of our approach’s effectiveness and scalability using three practical examples

Animating the Ethical Demand:Exploring user dispositions in industry innovation cases through animation-based sketching

This paper addresses the challenge of attaining ethical user stances during the design process of products and services and proposes animation-based sketching as a design method, which supports elaborating and examining different ethical stances towards the user. The discussion is qualified by an empirical study of Responsible Research and Innovation (RRI) in a Triple Helix constellation. Using a three-week long innovation workshop, UCrAc, involving 16 Danish companies and organisations and 142 students as empirical data, we discuss how animation-based sketching can explore not yet existing user dispositions, as well as create an incentive for ethical conduct in development and innovation processes. The ethical fulcrum evolves around Løgstrup's Ethical Demand and his notion of spontaneous life manifestations. From this, three ethical stances are developed; apathy, sympathy and empathy. By exploring both apathetic and sympathetic views, the ethical reflections are more nuanced as a result of actually seeing the user experience simulated through different user dispositions. Exploring the three ethical stances by visualising real use cases with the technologies simulated as already being implemented makes the life manifestations of the users in context visible. We present and discuss how animation-based sketching can support the elaboration and examination of different ethical stances towards the user in the product and service development process. Finally we present a framework for creating narrative representations of emerging technology use cases, which invite to reflection upon the ethics of the user experience.</jats:p

Who can help me? Reconstructing users' psychological journeys in depression-related social media interactions

Social media are increasingly being used as self-help boards, where individuals can disclose personal experiences and feelings and look for support from peers or experts. Here we investigate several popular mental health-related Reddit boards about depression while proposing a novel psycho-social framework. We reconstruct users' psychological/linguistic profiles together with their social interactions. We cover a total of 303,016 users, engaging in 378,483 posts and 1,475,044 comments from 01/05/2018 to 01/05/2020. After identifying a network of users' interactions, e.g., who replied to whom, we open an unprecedented window over psycholinguistic, cognitive, and affective digital traces with relevance for mental health research. Through user-generated content, we identify four categories or archetypes of users in agreement with the Patient Health Engagement model: the emotionally turbulent/under blackout, the aroused, the adherent-yet-conflicted, and the eudaimonically hopeful. Analyzing users' transitions over time through conditional Markov processes, we show how these four archetypes are not consecutive stages. We do not find a linear progression or sequential patient journey, where users evolve from struggling to serenity through feelings of conflict. Instead, we find online users to follow spirals towards both negative and positive archetypal stages. Through psychological/linguistic and social network modelling, we can provide compelling quantitative pieces of evidence on how such a complex path unfolds through positive, negative, and conflicting online contexts. Our approach opens the way to data-informed understandings of psychological coping with mental health issues through social media.Comment: main article + supporting informatio