7,637 research outputs found
What your Facebook Profile Picture Reveals about your Personality
People spend considerable effort managing the impressions they give others.
Social psychologists have shown that people manage these impressions
differently depending upon their personality. Facebook and other social media
provide a new forum for this fundamental process; hence, understanding people's
behaviour on social media could provide interesting insights on their
personality. In this paper we investigate automatic personality recognition
from Facebook profile pictures. We analyze the effectiveness of four families
of visual features and we discuss some human interpretable patterns that
explain the personality traits of the individuals. For example, extroverts and
agreeable individuals tend to have warm colored pictures and to exhibit many
faces in their portraits, mirroring their inclination to socialize; while
neurotic ones have a prevalence of pictures of indoor places. Then, we propose
a classification approach to automatically recognize personality traits from
these visual features. Finally, we compare the performance of our
classification approach to the one obtained by human raters and we show that
computer-based classifications are significantly more accurate than averaged
human-based classifications for Extraversion and Neuroticism
Cosine similarity-based algorithm for social networking recommendation
Social media have become a discussion platform for individuals and groups. Hence, users belonging to different groups can communicate together. Positive and negative messages as well as media are circulated between those users. Users can form special groups with people who they already know in real life or meet through social networking after being suggested by the system. In this article, we propose a framework for recommending communities to users based on their preferences; for example, a community for people who are interested in certain sports, art, hobbies, diseases, age, case, and so on. The framework is based on a feature extraction algorithm that utilizes user profiling and combines the cosine similarity measure with term frequency to recommend groups or communities. Once the data is received from the user, the system tracks their behavior, the relationships are identified, and then the system recommends one or more communities based on their preferences. Finally, experimental studies are conducted using a prototype developed to test the proposed framework, and results show the importance of our framework in recommending people to communities
An Enhanced Scammer Detection Model for Online Social Network Frauds Using Machine Learning
The prevalence of online social networking increase in the risk of social network scams or fraud. Scammers often create fake profiles to trick unsuspecting users into fraudulent activities. Therefore, it is important to be able to identify these scammer profiles and prevent fraud such as dating scams, compromised accounts, and fake profiles. This study proposes an enhanced scammer detection model that utilizes user profile attributes and images to identify scammer profiles in online social networks. The approach involves preprocessing user profile data, extracting features, and machine learning algorithms for classification. The system was tested on a dataset created specifically for this study and was found to have an accuracy rate of 94.50% with low false-positive rates. The proposed approach aims to detect scammer profiles early on to prevent online social network fraud and ensure a safer environment for society and women’s safety
Wireless Communications in the Era of Big Data
The rapidly growing wave of wireless data service is pushing against the
boundary of our communication network's processing power. The pervasive and
exponentially increasing data traffic present imminent challenges to all the
aspects of the wireless system design, such as spectrum efficiency, computing
capabilities and fronthaul/backhaul link capacity. In this article, we discuss
the challenges and opportunities in the design of scalable wireless systems to
embrace such a "bigdata" era. On one hand, we review the state-of-the-art
networking architectures and signal processing techniques adaptable for
managing the bigdata traffic in wireless networks. On the other hand, instead
of viewing mobile bigdata as a unwanted burden, we introduce methods to
capitalize from the vast data traffic, for building a bigdata-aware wireless
network with better wireless service quality and new mobile applications. We
highlight several promising future research directions for wireless
communications in the mobile bigdata era.Comment: This article is accepted and to appear in IEEE Communications
Magazin
Wearing Many (Social) Hats: How Different are Your Different Social Network Personae?
This paper investigates when users create profiles in different social
networks, whether they are redundant expressions of the same persona, or they
are adapted to each platform. Using the personal webpages of 116,998 users on
About.me, we identify and extract matched user profiles on several major social
networks including Facebook, Twitter, LinkedIn, and Instagram. We find evidence
for distinct site-specific norms, such as differences in the language used in
the text of the profile self-description, and the kind of picture used as
profile image. By learning a model that robustly identifies the platform given
a user's profile image (0.657--0.829 AUC) or self-description (0.608--0.847
AUC), we confirm that users do adapt their behaviour to individual platforms in
an identifiable and learnable manner. However, different genders and age groups
adapt their behaviour differently from each other, and these differences are,
in general, consistent across different platforms. We show that differences in
social profile construction correspond to differences in how formal or informal
the platform is.Comment: Accepted at the 11th International AAAI Conference on Web and Social
Media (ICWSM17
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