2,530 research outputs found
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
Are you Charlie or Ahmed? Cultural pluralism in Charlie Hebdo response on Twitter
We study the response to the Charlie Hebdo shootings of January 7, 2015 on
Twitter across the globe. We ask whether the stances on the issue of freedom of
speech can be modeled using established sociological theories, including
Huntington's culturalist Clash of Civilizations, and those taking into
consideration social context, including Density and Interdependence theories.
We find support for Huntington's culturalist explanation, in that the
established traditions and norms of one's "civilization" predetermine some of
one's opinion. However, at an individual level, we also find social context to
play a significant role, with non-Arabs living in Arab countries using
#JeSuisAhmed ("I am Ahmed") five times more often when they are embedded in a
mixed Arab/non-Arab (mention) network. Among Arabs living in the West, we find
a great variety of responses, not altogether associated with the size of their
expatriate community, suggesting other variables to be at play.Comment: International AAAI Conference on Web and Social Media (ICWSM), 201
Detecting Arabic Offensive Language in Microblogs Using Domain-Specific Word Embeddings and Deep Learning
In recent years, social media networks are emerging as a key player by providing platforms for opinions expression, communication, and content distribution. However, users often take advantage of perceived anonymity on social media platforms to share offensive or hateful content. Thus, offensive language has grown as a significant issue with the increase in online communication and the popularity of social media platforms. This problem has attracted significant attention for devising methods for detecting offensive content and preventing its spread on online social networks. Therefore, this paper aims to develop an effective Arabic offensive language detection model by employing deep learning and semantic and contextual features. This paper proposes a deep learning approach that utilizes the bidirectional long short-term memory (BiLSTM) model and domain-specific word embeddings extracted from an Arabic offensive dataset. The detection approach was evaluated on an Arabic dataset collected from Twitter. The results showed the highest performance accuracy of 0.93% with the BiLSTM model trained using a combination of domain-specific and agnostic-domain word embeddings
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