13 research outputs found
Tweets as impact indicators: Examining the implications of automated bot accounts on Twitter
This brief communication presents preliminary findings on automated Twitter
accounts distributing links to scientific papers deposited on the preprint
repository arXiv. It discusses the implication of the presence of such bots
from the perspective of social media metrics (altmetrics), where mentions of
scholarly documents on Twitter have been suggested as a means of measuring
impact that is both broader and timelier than citations. We present preliminary
findings that automated Twitter accounts create a considerable amount of tweets
to scientific papers and that they behave differently than common social bots,
which has critical implications for the use of raw tweet counts in research
evaluation and assessment. We discuss some definitions of Twitter cyborgs and
bots in scholarly communication and propose differentiating between different
levels of engagement from tweeting only bibliographic information to discussing
or commenting on the content of a paper.Comment: 9 pages, 4 figures, 1 tabl
Manipulating the Online Marketplace of Ideas
Social media, the modern marketplace of ideas, is vulnerable to manipulation.
Deceptive inauthentic actors impersonate humans to amplify misinformation and
influence public opinions. Little is known about the large-scale consequences
of such operations, due to the ethical challenges posed by online experiments
that manipulate human behavior. Here we introduce a model of information
spreading where agents prefer quality information but have limited attention.
We evaluate the impact of manipulation strategies aimed at degrading the
overall quality of the information ecosystem. The model reproduces empirical
patterns about amplification of low-quality information. We find that
infiltrating a critical fraction of the network is more damaging than
generating attention-grabbing content or targeting influentials. We discuss
countermeasures suggested by these insights to increase the resilience of
social media users to manipulation, and legal issues arising from regulations
aimed at protecting human speech from suppression by inauthentic actors.Comment: 25 pages, 8 figures, 80 reference
Automated Crowdturfing Attacks and Defenses in Online Review Systems
Malicious crowdsourcing forums are gaining traction as sources of spreading
misinformation online, but are limited by the costs of hiring and managing
human workers. In this paper, we identify a new class of attacks that leverage
deep learning language models (Recurrent Neural Networks or RNNs) to automate
the generation of fake online reviews for products and services. Not only are
these attacks cheap and therefore more scalable, but they can control rate of
content output to eliminate the signature burstiness that makes crowdsourced
campaigns easy to detect.
Using Yelp reviews as an example platform, we show how a two phased review
generation and customization attack can produce reviews that are
indistinguishable by state-of-the-art statistical detectors. We conduct a
survey-based user study to show these reviews not only evade human detection,
but also score high on "usefulness" metrics by users. Finally, we develop novel
automated defenses against these attacks, by leveraging the lossy
transformation introduced by the RNN training and generation cycle. We consider
countermeasures against our mechanisms, show that they produce unattractive
cost-benefit tradeoffs for attackers, and that they can be further curtailed by
simple constraints imposed by online service providers
An exploratory data analysis of the #Crowdfunding network on Twitter
Together, social media and crowdsourcing can help entrepreneurs to attract external
finance and early-stage customers. This paper investigates the characteristics and discourse of an
issue-centered public on Twitter organized around the hashtag #crowdfunding through the lens of
social network theory. Using a dataset of 2,732,144 tweets published during a calendar year, we use
exploratory data analysis to generate insights and hypotheses on who the users in the #crowdfunding
network are, what they share, and how they are connected to each other. In order to do so, we adopt
a range of descriptive, content, network analytics techniques. The results suggest that platforms,
crowdfunders, and other actors who derive income from the crowdfunding economy play a key
role in creating the network. Furthermore, latent ties (strangers) play a direct role in disseminating
information, investing, and sending signals to platforms that further raises campaign prominence.
We also introduce a new type of social tie, the âcomputer as a social actorâ, previously unaddressed
in entrepreneurial network literature, which play a role in sending signals to both platforms and
networks. Our results suggest that homophily is a key driver for creating network sub-communities
built around specific platforms, project types, domains, or geograph
Artificial intelligence crime: an interdisciplinary analysis of foreseeable threats and solutions
Artificial Intelligence (AI) research and regulation seek to balance the benefits of innovation against any potential harms and disruption. However, one unintended consequence of the recent surge in AI research is the potential re-orientation of AI technologies to facilitate criminal acts, which we term AI-Crime (AIC). We already know that AIC is theoretically feasible thanks to published experiments in automating fraud targeted at social media users, as well as demonstrations of AI-driven manipulation of simulated markets. However, because AIC is still a relatively young and inherently interdisciplinary areaâspanning socio-legal studies to formal scienceâthere is little certainty of what an AIC future might look like. This article offers the first systematic, interdisciplinary literature analysis of the foreseeable threats of AIC, providing law enforcement and policy-makers with a synthesis of the current problems, and a possible solution space
Sorting the Healthy Diet Signal from the Social Media Expert Noise: Preliminary Evidence from the Healthy Diet Discourse on Twitter
: Over 2.8 million people die each year from being overweight or obese, a largely preventable
disease. Social media has fundamentally changed the way we communicate, collaborate, consume,
and create content. The ease with which content can be shared has resulted in a rapid increase
in the number of individuals or organisations that seek to influence opinion and the volume of
content that they generate. The nutrition and diet domain is not immune to this phenomenon.
Unfortunately, from a public health perspective, many of these âinfluencersâ may be poorly qualified
in order to provide nutritional or dietary guidance, and advice given may be without accepted
scientific evidence and contrary to public health policy. In this preliminary study, we analyse the
âhealthy dietâ discourse on Twitter. While using a multi-component analytical approach, we analyse
more than 1.2 million English language tweets over a 16-month period in order to identify and
characterise the influential actors and discover topics of interest in the discourse. Our analysis
suggests that the discourse is dominated by non-health professionals. There is widespread use of
bots that pollute the discourse and seek to create a false equivalence on the efficacy of a particular
nutritional strategy or diet. Topic modelling suggests a significant focus on diet, nutrition, exercise,
weight, disease, and quality of life. Public health policy makers and professional nutritionists need to
consider what interventions can be taken in order to counteract the influence of non-professional and
bad actors on social media
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Campaign spheres in Latin America: How institutions affect digital media in presidential elections
The changes in the media environment brought up by digital media are expected to have profound effects on politics, from citizenâs participation to elitesâ strategies. However, the literature has paid little attention to systemic and contextual factors that might constrain or limit the possibilities for collective action in this new communication context. Using a structural approach, I analyze presidential election campaignsâ use of Twitter in a study of 16 Latin American countries between 2012 and 2015 â Argentina, Bolivia, Brazil, Chile, Colombia, Costa Rica, Dominican Republic, Ecuador, El Salvador, Guatemala, Honduras, Mexico, Panama, Paraguay, Uruguay and Venezuela. To demonstrate the quality of the data set, I show that forecasting models with Twitter Volume can preview election results in most of the cases, and even give more accurate forecasting than polling data in Colombia, Costa Rica, Ecuador and Mexico. In order to fill the shortage of theoretical models on social media, I propose a new concept of campaign spheres for understanding the linkage between social media messages and public opinion associated with campaigns. Campaign spheres are sections of the public sphere. Instead of forming public opinion through rational deliberation, a campaign sphere mostly reproduces a candidateâs political slogans and symbols to mobilize opinion leaders. While the public sphere is inclusive, a campaign sphere has clear boundaries dividing those who support the campaign and those who oppose it. Institutions and social context do affect campaign spheres in Latin American presidential elections. Party systems can predict how intensely people participate, while incumbency increases the number of participants, mainly in left-wing campaign spheres. Two-round systems had a negative impact on the participation of right-wing campaign spheres, and also demonstrated a small negative effect on the prestige of all candidates. Yet challengers had more prestige than incumbents â an effect that was stronger in plurality system countries and after the elections. This research gives a perspective on the constrained context for communication change in Latin America, which can be extended and generalized to other new democracies