Do Social Bots Dream of Electric Sheep? A Categorisation of Social Media Bot Accounts

So-called 'social bots' have garnered a lot of attention lately. Previous research showed that they attempted to influence political events such as the Brexit referendum and the US presidential elections. It remains, however, somewhat unclear what exactly can be understood by the term 'social bot'. This paper addresses the need to better understand the intentions of bots on social media and to develop a shared understanding of how 'social' bots differ from other types of bots. We thus describe a systematic review of publications that researched bot accounts on social media. Based on the results of this literature review, we propose a scheme for categorising bot accounts on social media sites. Our scheme groups bot accounts by two dimensions - Imitation of human behaviour and Intent.Comment: Accepted for publication in the Proceedings of the Australasian Conference on Information Systems, 201

Twitter Bot Detection

In this thesis, I explore the identification of non-human Twitter users. I am interested in classifying users by behavior into the categories of either bot or human. My goal in this research is to find an accurate and efficient means of identifying and segregating non-human Twitter users from their human counterparts. I use a two-stage data collection process to collect Twitter users suspected of being a bot and then obtain a majority vote on the suspected users to validate the suspicion. I gather, on average, 1000 tweets per user, on which I calculate 40 features characterizing the user. I explore the effectiveness of three different methods to most accurately classify users as either a bot or a human based on these features. The results of this work show that bots can be classified efficiently and with a high degree of accuracy. I show that certain features play a larger role in the classification process than others. The applications of Twitter bot identification include: (i) protecting users from malicious content (ii) spam filtering, and (iii) bot removal from Twitter data for other research

Online Human-Bot Interactions: Detection, Estimation, and Characterization

Increasing evidence suggests that a growing amount of social media content is generated by autonomous entities known as social bots. In this work we present a framework to detect such entities on Twitter. We leverage more than a thousand features extracted from public data and meta-data about users: friends, tweet content and sentiment, network patterns, and activity time series. We benchmark the classification framework by using a publicly available dataset of Twitter bots. This training data is enriched by a manually annotated collection of active Twitter users that include both humans and bots of varying sophistication. Our models yield high accuracy and agreement with each other and can detect bots of different nature. Our estimates suggest that between 9% and 15% of active Twitter accounts are bots. Characterizing ties among accounts, we observe that simple bots tend to interact with bots that exhibit more human-like behaviors. Analysis of content flows reveals retweet and mention strategies adopted by bots to interact with different target groups. Using clustering analysis, we characterize several subclasses of accounts, including spammers, self promoters, and accounts that post content from connected applications.Comment: Accepted paper for ICWSM'17, 10 pages, 8 figures, 1 tabl

A Posthuman-Xenofeminist Analysis of the Discourse on Autonomous Weapons Systems and Other Killing Machines

In this article, I critique the current debates surrounding autonomous weapons systems, using feminist posthuman theory to make sense of such systems – and the relation between human and machine – in terms of automation and autonomy. The dominant narratives about autonomous weapons tend to present them as exceptional; they are distinct from all the other kinds of human inventions that can kill. Further attention is required, not on autonomous weapons themselves but on the delegation of killing to a far broader range of technologies across the human–machine/autonomous–automated spectrum. While current attempts at legal regulation distinguish between civil and military technologies, such a distinction becomes impossible in light of the links between civil and military technologies and the killing potential of many technologies, including artificial intelligence

Detecting Abnormal Behavior in Web Applications

The rapid advance of web technologies has made the Web an essential part of our daily lives. However, network attacks have exploited vulnerabilities of web applications, and caused substantial damages to Internet users. Detecting network attacks is the first and important step in network security. A major branch in this area is anomaly detection. This dissertation concentrates on detecting abnormal behaviors in web applications by employing the following methodology. For a web application, we conduct a set of measurements to reveal the existence of abnormal behaviors in it. We observe the differences between normal and abnormal behaviors. By applying a variety of methods in information extraction, such as heuristics algorithms, machine learning, and information theory, we extract features useful for building a classification system to detect abnormal behaviors.;In particular, we have studied four detection problems in web security. The first is detecting unauthorized hotlinking behavior that plagues hosting servers on the Internet. We analyze a group of common hotlinking attacks and web resources targeted by them. Then we present an anti-hotlinking framework for protecting materials on hosting servers. The second problem is detecting aggressive behavior of automation on Twitter. Our work determines whether a Twitter user is human, bot or cyborg based on the degree of automation. We observe the differences among the three categories in terms of tweeting behavior, tweet content, and account properties. We propose a classification system that uses the combination of features extracted from an unknown user to determine the likelihood of being a human, bot or cyborg. Furthermore, we shift the detection perspective from automation to spam, and introduce the third problem, namely detecting social spam campaigns on Twitter. Evolved from individual spammers, spam campaigns manipulate and coordinate multiple accounts to spread spam on Twitter, and display some collective characteristics. We design an automatic classification system based on machine learning, and apply multiple features to classifying spam campaigns. Complementary to conventional spam detection methods, our work brings efficiency and robustness. Finally, we extend our detection research into the blogosphere to capture blog bots. In this problem, detecting the human presence is an effective defense against the automatic posting ability of blog bots. We introduce behavioral biometrics, mainly mouse and keyboard dynamics, to distinguish between human and bot. By passively monitoring user browsing activities, this detection method does not require any direct user participation, and improves the user experience

Hacia la sexualización de la inteligência artificial (IA); proyección y representación de la consciencia sexual en cuerpos y entidades posthumanos

The dissertation analizes the sexualization of the AI and how nowadays this phenomenon occurs in the technological production and in the sci-fi narrative. We observe a strong boundary between the progress in the scientific community and some of the fictional production analyzed throughout the investigation. We give an insight into the Japanese and USA rotobotic and cyber culture, the two leading countries in AI research and pop – culture production. We focus on the problematic reception of this new industrial revolution, trying to understand anxieties and hopes. Finally, we face the problem of gendered machines, which is a prejudicial and sometimes sexist, programming. We dedicate the last chapter to three movies: Ghost in the Shell, Her and Ex Machina, the greatest cinematic examples of futuristic theories on female cyborg and artificial intelligence. They are movies where the exclusive prerogatives of humanity, such as seduction, love, need for a body, instinct of reproduction and death, are reinterpreted by the machines. To address these issues, we have worked on scientific sources and philosophical texts linked to feminism and post-humanism. We have trained our anthropological look in the analysis of society, relying our vision on the future of science fiction production. The conclusions will shed light on the specular resemble between man and machine, where the danger does not seem to be just the humanization of machines, but also the progressive mechanization of mankind. This future prompts us to be ready to remodel our sense of both individuality and community, in a new reassessment of those parameters that today constitute humans

Loyalty cards and the problem of CAPTCHA: 2nd tier security and usability issues for senior citizens

Information Security often works in antipathy to access and useability in communities of older citizens. Whilst security features are required to prevent the disclosure of information, some security tools have a deleterious effect upon users, resulting in insecure practices. Security becomes unfit for purpose where users prefer to abandon applications and online benefits in favour of non-digital authentication and verification requirements. For some, the ability to read letters and symbols from a distorted image is a decidedly more difficult task than for others, and the resulting level of security from CAPTCHA tests is not consistent from person to person. This paper discusses the changing paradigm regarding second tier applications where non-essential benefits are forgone in order to avoid the frustration, uncertainty and humiliation of repeated failed attempts to access online software by means of CAPTCHA

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