Seminar Users in the Arabic Twitter Sphere

We introduce the notion of "seminar users", who are social media users engaged in propaganda in support of a political entity. We develop a framework that can identify such users with 84.4% precision and 76.1% recall. While our dataset is from the Arab region, omitting language-specific features has only a minor impact on classification performance, and thus, our approach could work for detecting seminar users in other parts of the world and in other languages. We further explored a controversial political topic to observe the prevalence and potential potency of such users. In our case study, we found that 25% of the users engaged in the topic are in fact seminar users and their tweets make nearly a third of the on-topic tweets. Moreover, they are often successful in affecting mainstream discourse with coordinated hashtag campaigns.Comment: to appear in SocInfo 201

Effectiveness of dismantling strategies on moderated vs. unmoderated online social platforms

Online social networks are the perfect test bed to better understand large-scale human behavior in interacting contexts. Although they are broadly used and studied, little is known about how their terms of service and posting rules affect the way users interact and information spreads. Acknowledging the relation between network connectivity and functionality, we compare the robustness of two different online social platforms, Twitter and Gab, with respect to dismantling strategies based on the recursive censor of users characterized by social prominence (degree) or intensity of inflammatory content (sentiment). We find that the moderated (Twitter) vs unmoderated (Gab) character of the network is not a discriminating factor for intervention effectiveness. We find, however, that more complex strategies based upon the combination of topological and content features may be effective for network dismantling. Our results provide useful indications to design better strategies for countervailing the production and dissemination of anti-social content in online social platforms

Current Unanswered Questions in Social Media Activism Research

For the last decade, researchers have conducted numerous research studies on the role various social media platforms have been used by activists to affect change. These general surveys and specific analyses address how media sites are being used in the specific roles they currently play or have played in a given movement. Immeasurable time and effort has been regularly dedicated to understand the impact these platforms have had on social change. However, social media sites continue to change and evolve over time, creating new opportunities for investigation. This transitional article is intended to propose and contextualize some of the current questions related to the largest platforms in social media activism, with the intent to suggest direction for such future research

When users control the algorithms: Values expressed in practices on the twitter platform

Recent interest in ethical AI has brought a slew of values, including fairness, into conversations about technology design. Research in the area of algorithmic fairness tends to be rooted in questions of distribution that can be subject to precise formalism and technical implementation. We seek to expand this conversation to include the experiences of people subject to algorithmic classification and decision-making. By examining tweets about the “Twitter algorithm” we consider the wide range of concerns and desires Twitter users express. We find a concern with fairness (narrowly construed) is present, particularly in the ways users complain that the platform enacts a political bias against conservatives. However, we find another important category of concern, evident in attempts to exert control over the algorithm. Twitter users who seek control do so for a variety of reasons, many well justified. We argue for the need for better and clearer definitions of what constitutes legitimate and illegitimate control over algorithmic processes and to consider support for users who wish to enact their own collective choices

Twitter, Social Services and Covid-19: Analysis of Interactions between Political Parties and Citizens

The state of alarm caused by Covid-19 has mobilised the population’s digital social participation in social networks. Likewise, the relevance acquired by Social Services as a support for the social and health crisis has generated an unprecedented social debate on Twitter about the reality of these services in Spain. The analysis of this phenomenon is the focus of the present article, in which the tweets on Social Services and Covid-19 published during the confinement have been analysed using the qualitative analysis software Atlas.Ti. The results show the precariousness of social services and that a change in the management and financing model of these services is required to guarantee benefits and satisfy fundamental social rights

Online social networking, order and disorder

Whilst online social networking has been used successfully for many years by all strata of the world’s population, its use to ferment and prevent civil disturbances is a relatively new phenomenon. It is clear that the way in which online social networking sites are being used is evolving, and that changing user perceptions of online privacy may impact on the ability of the law enforcement community to adapt to new methods of monitoring and evidence gathering. This paper focuses primarily on the London riots of August 2011, and as such discusses legal issues from a UK perspective. However, the matters discussed are of relevance worldwide, with reference made to similar events outside the UK, to show that what occurred in London was not an isolated incident, or a quirk of the UK social networking scene. This paper explores what occurred, the platforms that were used and how they were used, and the legal framework in which investigations took place. It examines the use of social networking to organise rioters, support community defence, and shape the response of law enforcement agencies such as the police, government and the courts. It concludes that there is significant potential for problems of this type to occur in the future, which will require the evolution of law enforcement methods and procedures, and could change the way in which the law enforcement community utilise e-Government systems

Smart working during the Covid19 pandemic in Italy: Twitter narratives in female-centered communities.

While the recent pandemic has accelerated the spread of smart working dynamics in Italy, social media increased their importance as platforms to vehiculate information and points of view and shape public opinion. In the face of extended confinement and a looming health crisis, society has had to fundamentally rethink its daily work practices, social relations, family relationship management, and work-life balance. As a result, the radical and abrupt migration to networked platforms has been a disruptive and unprecedented phenomenon. We aimed to investigate the Twitter debate on smart working during the pandemic by focusing mainly on social concerns and thematics related to work-life balance by addressing the following research questions: RQ1: How was the topic of smart working debated on Twitter during the Covid19 pandemic (2020-2021) in Italy, and which narratives and issues fuelled the debate the most? RQ2: How the public debate has received the Italian government's worklife balance measures?RQ3: Which topics were most discussed by women on smart working? We used Digital Methods to cope with re-proposing data to depict collective phenomena, social transformations, and cultural expressions by analyzing natively digital data on social media platforms. We gathered more than 750.000 tweets between 28 February 2020 and 30 November 2021, and we mapped narratives and communities by using social network analysis. This allowed for the selection of the more intriguing ones to define various sub-datasets on which to conduct a topic modeling study, which aided in understanding more nuanced aspects of the highly fragmented topic. By studying the italian debate, we identified specific communities which debated government measures to help families during the pandemic and discussed digitalization and smart working as a new paradigm for work. We found DAD (Didactic at Distance, aka homeschooling) as a transversal topic that highly affected how people experienced smart working

Understanding Bots on Social Media - An Application in Disaster Response

abstract: Social media has become a primary platform for real-time information sharing among users. News on social media spreads faster than traditional outlets and millions of users turn to this platform to receive the latest updates on major events especially disasters. Social media bridges the gap between the people who are affected by disasters, volunteers who offer contributions, and first responders. On the other hand, social media is a fertile ground for malicious users who purposefully disturb the relief processes facilitated on social media. These malicious users take advantage of social bots to overrun social media posts with fake images, rumors, and false information. This process causes distress and prevents actionable information from reaching the affected people. Social bots are automated accounts that are controlled by a malicious user and these bots have become prevalent on social media in recent years. In spite of existing efforts towards understanding and removing bots on social media, there are at least two drawbacks associated with the current bot detection algorithms: general-purpose bot detection methods are designed to be conservative and not label a user as a bot unless the algorithm is highly confident and they overlook the effect of users who are manipulated by bots and (unintentionally) spread their content. This study is trifold. First, I design a Machine Learning model that uses content and context of social media posts to detect actionable ones among them; it specifically focuses on tweets in which people ask for help after major disasters. Second, I focus on bots who can be a facilitator of malicious content spreading during disasters. I propose two methods for detecting bots on social media with a focus on the recall of the detection. Third, I study the characteristics of users who spread the content of malicious actors. These features have the potential to improve methods that detect malicious content such as fake news.Dissertation/ThesisDoctoral Dissertation Computer Science 201

Discovering and Mitigating Social Data Bias

abstract: Exabytes of data are created online every day. This deluge of data is no more apparent than it is on social media. Naturally, finding ways to leverage this unprecedented source of human information is an active area of research. Social media platforms have become laboratories for conducting experiments about people at scales thought unimaginable only a few years ago. Researchers and practitioners use social media to extract actionable patterns such as where aid should be distributed in a crisis. However, the validity of these patterns relies on having a representative dataset. As this dissertation shows, the data collected from social media is seldom representative of the activity of the site itself, and less so of human activity. This means that the results of many studies are limited by the quality of data they collect. The finding that social media data is biased inspires the main challenge addressed by this thesis. I introduce three sets of methodologies to correct for bias. First, I design methods to deal with data collection bias. I offer a methodology which can find bias within a social media dataset. This methodology works by comparing the collected data with other sources to find bias in a stream. The dissertation also outlines a data collection strategy which minimizes the amount of bias that will appear in a given dataset. It introduces a crawling strategy which mitigates the amount of bias in the resulting dataset. Second, I introduce a methodology to identify bots and shills within a social media dataset. This directly addresses the concern that the users of a social media site are not representative. Applying these methodologies allows the population under study on a social media site to better match that of the real world. Finally, the dissertation discusses perceptual biases, explains how they affect analysis, and introduces computational approaches to mitigate them. The results of the dissertation allow for the discovery and removal of different levels of bias within a social media dataset. This has important implications for social media mining, namely that the behavioral patterns and insights extracted from social media will be more representative of the populations under study.Dissertation/ThesisDoctoral Dissertation Computer Science 201