968 research outputs found
White, Man, and Highly Followed: Gender and Race Inequalities in Twitter
Social media is considered a democratic space in which people connect and
interact with each other regardless of their gender, race, or any other
demographic factor. Despite numerous efforts that explore demographic factors
in social media, it is still unclear whether social media perpetuates old
inequalities from the offline world. In this paper, we attempt to identify
gender and race of Twitter users located in U.S. using advanced image
processing algorithms from Face++. Then, we investigate how different
demographic groups (i.e. male/female, Asian/Black/White) connect with other. We
quantify to what extent one group follow and interact with each other and the
extent to which these connections and interactions reflect in inequalities in
Twitter. Our analysis shows that users identified as White and male tend to
attain higher positions in Twitter, in terms of the number of followers and
number of times in user's lists. We hope our effort can stimulate the
development of new theories of demographic information in the online space.Comment: In Proceedings of the IEEE/WIC/ACM International Conference on Web
Intelligence (WI'17). Leipzig, Germany. August 201
The Anatomy of Conspirators: Unveiling Traits using a Comprehensive Twitter Dataset
The discourse around conspiracy theories is currently thriving amidst the
rampant misinformation prevalent in online environments. Research in this field
has been focused on detecting conspiracy theories on social media, often
relying on limited datasets. In this study, we present a novel methodology for
constructing a Twitter dataset that encompasses accounts engaged in
conspiracy-related activities throughout the year 2022. Our approach centers on
data collection that is independent of specific conspiracy theories and
information operations. Additionally, our dataset includes a control group
comprising randomly selected users who can be fairly compared to the
individuals involved in conspiracy activities. This comprehensive collection
effort yielded a total of 15K accounts and 37M tweets extracted from their
timelines. We conduct a comparative analysis of the two groups across three
dimensions: topics, profiles, and behavioral characteristics. The results
indicate that conspiracy and control users exhibit similarity in terms of their
profile metadata characteristics. However, they diverge significantly in terms
of behavior and activity, particularly regarding the discussed topics, the
terminology used, and their stance on trending subjects. Interestingly, there
is no significant disparity in the presence of bot users between the two
groups, suggesting that conspiracy and automation are orthogonal concepts.
Finally, we develop a classifier to identify conspiracy users using 93
features, some of which are commonly employed in literature for troll
identification. The results demonstrate a high accuracy level (with an average
F1 score of 0.98%), enabling us to uncover the most discriminative features
associated with conspiracy-related accounts
Massive Open Online Courses as affinity spaces for connected learning: Exploring effective learning interactions in one massive online community
This paper describes a participatory online culture – Connected Learning Massive Open Online Collaboration (CLMOOC) – and asks how its ethos of reciprocity and creative playfulness occurs. By analysing Twitter interactions over a four-week period, we conclude that this is due to the supportive nature of participants, who describe themselves as belonging to, or connected with, the community. We suggest that Gee’s concept of an affinity space is an appropriate model for CLMOOC and ask how this might be replicated in a higher education setting
The refugee/migrant crisis dichotomy on twitter: A network and sentiment perspective
Media reports, political statements, and social media debates on the refugee/migrant crisis shape the ways in which people and societies respond to those displaced people arriving at their borders world wide. These current events are framed and experienced as a crisis, entering the media, capturing worldwide political attention, and producing diverse and contradictory discourses and responses. The labels “migrant” and “refugee” are frequently distinguished and conflated in traditional as well as social media when describing the same groups of people. In this paper, we focus on the simultaneous struggle over meaning, legitimization, and power in representations of the refugee crisis, through the specific lens of Twitter. The 369,485 tweets analyzed in this paper cover two days after a picture of Alan Kurdi - a three-year-old Syrian boy who drowned in the Mediterranean Sea while trying to reach Europe with his family - made global headlines and sparked wide media engagement. More specifically, we investigate the existence of the dichotomy between the “deserving” refugee versus the “undeserving” migrant, as well as the relationship between sentiment expressed in tweets, their influence, and the popularity of Twitter users involved in this dichotomous characterization of the crisis. Our results show that the Twitter debate was predominantly focused on refugee related hashtags and that those tweets containing such hashtags were more positive in tone. Furthermore, we find that popular Twitter users as well as popular tweets are characterized by less emotional intensity and slightly less positivity in the debate, contrary to prior expectations. Co-occurrence networks expose the structure underlying hashtag usage and reveal a refugee-centric core of meaning, yet divergent goals of some prominent users. As social media become increasingly prominent venues for debate over a crisis, how and why people express their opinions offer valuable insights into the nature and direction of these debates
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The IMPED Model of Information Quality
This paper introduces a model for detecting low-quality information we refer to as the Index of Measured-diversity, Partisan-certainty, Ephemerality, and Domain (IMPED). The model purports that low-quality information is characterized by ephemerality, as opposed to quality content that is designed for permanence. The IMPED model leverages linguistic and temporal patterns in the content of social media messages and linked webpages to estimate a parametric survival model and the likelihood the content will be removed from the Internet. We review the limitations of current approaches for the detection of problematic content, including misinformation and false news, which are largely based on fact-checking and machine learning, and detail the requirements for a successful implementation of the IMPED model. The paper concludes with a review of examples taken from the 2018 election cycle and the performance of the model in identifying low-quality information as a proxy for problematic content
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