46 research outputs found
Popularity Evolution of Professional Users on Facebook
Popularity in social media is an important objective for professional users
(e.g. companies, celebrities, and public figures, etc). A simple yet prominent
metric utilized to measure the popularity of a user is the number of fans or
followers she succeed to attract to her page. Popularity is influenced by
several factors which identifying them is an interesting research topic. This
paper aims to understand this phenomenon in social media by exploring the
popularity evolution for professional users in Facebook. To this end, we
implemented a crawler and monitor the popularity evolution trend of 8k most
popular professional users on Facebook over a period of 14 months. The
collected dataset includes around 20 million popularity values and 43 million
posts. We characterized different popularity evolution patterns by clustering
the users temporal number of fans and study them from various perspectives
including their categories and level of activities. Our observations show that
being active and famous correlate positively with the popularity trend
Middleware Technologies for Cloud of Things - a survey
The next wave of communication and applications rely on the new services
provided by Internet of Things which is becoming an important aspect in human
and machines future. The IoT services are a key solution for providing smart
environments in homes, buildings and cities. In the era of a massive number of
connected things and objects with a high grow rate, several challenges have
been raised such as management, aggregation and storage for big produced data.
In order to tackle some of these issues, cloud computing emerged to IoT as
Cloud of Things (CoT) which provides virtually unlimited cloud services to
enhance the large scale IoT platforms. There are several factors to be
considered in design and implementation of a CoT platform. One of the most
important and challenging problems is the heterogeneity of different objects.
This problem can be addressed by deploying suitable "Middleware". Middleware
sits between things and applications that make a reliable platform for
communication among things with different interfaces, operating systems, and
architectures. The main aim of this paper is to study the middleware
technologies for CoT. Toward this end, we first present the main features and
characteristics of middlewares. Next we study different architecture styles and
service domains. Then we presents several middlewares that are suitable for CoT
based platforms and lastly a list of current challenges and issues in design of
CoT based middlewares is discussed.Comment: http://www.sciencedirect.com/science/article/pii/S2352864817301268,
Digital Communications and Networks, Elsevier (2017
Middleware Technologies for Cloud of Things - a survey
The next wave of communication and applications rely on the new services
provided by Internet of Things which is becoming an important aspect in human
and machines future. The IoT services are a key solution for providing smart
environments in homes, buildings and cities. In the era of a massive number of
connected things and objects with a high grow rate, several challenges have
been raised such as management, aggregation and storage for big produced data.
In order to tackle some of these issues, cloud computing emerged to IoT as
Cloud of Things (CoT) which provides virtually unlimited cloud services to
enhance the large scale IoT platforms. There are several factors to be
considered in design and implementation of a CoT platform. One of the most
important and challenging problems is the heterogeneity of different objects.
This problem can be addressed by deploying suitable "Middleware". Middleware
sits between things and applications that make a reliable platform for
communication among things with different interfaces, operating systems, and
architectures. The main aim of this paper is to study the middleware
technologies for CoT. Toward this end, we first present the main features and
characteristics of middlewares. Next we study different architecture styles and
service domains. Then we presents several middlewares that are suitable for CoT
based platforms and lastly a list of current challenges and issues in design of
CoT based middlewares is discussed.Comment: http://www.sciencedirect.com/science/article/pii/S2352864817301268,
Digital Communications and Networks, Elsevier (2017
Hate Speech Detection and Racial Bias Mitigation in Social Media based on BERT model
Disparate biases associated with datasets and trained classifiers in hateful
and abusive content identification tasks have raised many concerns recently.
Although the problem of biased datasets on abusive language detection has been
addressed more frequently, biases arising from trained classifiers have not yet
been a matter of concern. Here, we first introduce a transfer learning approach
for hate speech detection based on an existing pre-trained language model
called BERT and evaluate the proposed model on two publicly available datasets
annotated for racism, sexism, hate or offensive content on Twitter. Next, we
introduce a bias alleviation mechanism in hate speech detection task to
mitigate the effect of bias in training set during the fine-tuning of our
pre-trained BERT-based model. Toward that end, we use an existing
regularization method to reweight input samples, thereby decreasing the effects
of high correlated training set' s n-grams with class labels, and then
fine-tune our pre-trained BERT-based model with the new re-weighted samples. To
evaluate our bias alleviation mechanism, we employ a cross-domain approach in
which we use the trained classifiers on the aforementioned datasets to predict
the labels of two new datasets from Twitter, AAE-aligned and White-aligned
groups, which indicate tweets written in African-American English (AAE) and
Standard American English (SAE) respectively. The results show the existence of
systematic racial bias in trained classifiers as they tend to assign tweets
written in AAE from AAE-aligned group to negative classes such as racism,
sexism, hate, and offensive more often than tweets written in SAE from
White-aligned. However, the racial bias in our classifiers reduces
significantly after our bias alleviation mechanism is incorporated. This work
could institute the first step towards debiasing hate speech and abusive
language detection systems.Comment: This paper has been accepted in the PLOS ONE journal in August 202