20 research outputs found
Analysing and using subjective criteria to improve dental care recommendation systems
Online reviews and rating sites are shaping industries as the users rely on recommendations given by former consumers and sharing opinions on the web. Dentistry has also been impacted by dental patients' reviews. This paper classifies trust-related information for dental care recommendations onto 4 categories: context, relationship, reputation and subjective criteria. It discusses each category and describes how they help focussing on trust when matching patients and dentists in brief. The paper then focuses on subjective criteria and presents the results of a survey aimed at showing trustrelated information emerged from subjective characteristics. Traits of personalities are used as subjective characteristics of patients and that of dentists are derived from the online patients' reviews. 580 Australian patients were surveyed to determine what factors affect their decision to find the trusted dentist. Subjective characteristics of dentists such as dentists' qualities and experienced dentists are considered the most important factors after location and cost. The most preferred dentists' qualities by almost all types of personalities are experienced, professional and quality of service. When the patients are further classified based on levels of fear, their preferences for dentists' qualities changed. Subjective qualities of both patients and dentists are important factors to improve the matching capability for the dental care recommendation systems
To what extent homophily and influencer networks explain song popularity
Forecasting the popularity of new songs has become a standard practice in the
music industry and provides a comparative advantage for those that do it well.
Considerable efforts were put into machine learning prediction models for that
purpose. It is known that in these models, relevant predictive parameters
include intrinsic lyrical and acoustic characteristics, extrinsic factors
(e.g., publisher influence and support), and the previous popularity of the
artists. Much less attention was given to the social components of the
spreading of song popularity. Recently, evidence for musical homophily - the
tendency that people who are socially linked also share musical tastes - was
reported. Here we determine how musical homophily can be used to predict song
popularity. The study is based on an extensive dataset from the last.fm online
music platform from which we can extract social links between listeners and
their listening patterns. To quantify the importance of networks in the
spreading of songs that eventually determines their popularity, we use musical
homophily to design a predictive influence parameter and show that its
inclusion in state-of-the-art machine learning models enhances predictions of
song popularity. The influence parameter improves the prediction precision
(TP/(TP+FN)) by about 50% from 0.14 to 0.21, indicating that the social
component in the spreading of music plays at least as significant a role as the
artist's popularity or the impact of the genre.Comment: 7 pages, 3 figure
Media and Misinformation in Times of COVID-19: How People Informed Themselves in the Days Following the Portuguese Declaration of the State of Emergency
This study takes as a starting point the importance and dependence of the media to obtain information about the pandemic. The dependency theory of the media system was developed in the 1970s when mass media were the dominant source of information. Today, at a time when media choices have become abundant, studies are needed to understand the phenomenon of media dependence in light of new dimensions made important by the transformations that have taken place in the social and media fields—where the coexistence of mass media with social media platforms stands out. As large-scale crises rarely occur and the media environment changes rapidly, it is important to analyze how media dependence relates to choose and trust in different media (traditional media vs. social media) in times of crisis. Several questions arise. What is the trust attributed by individuals to social media as sources of information about COVID-19? How well informed are the individuals who choose these sources as the main sources of information? From a questionnaire administered to 244 individuals in Portugal, during the first week of the state of emergency (March 2020), this research seeks to identify how people gained access to information about COVID-19, how they acted critically towards the various sources and how they assess the reliability of different media. Finally, it analyzes the association between the type of medium chosen and adherence to misinformation content about the virus. The results reveal the existence of a phenomenon of dependence on the media, with a strong exposure (both active and accidental) to informative content, with conventional media being privileged as the main source, and positively distinguished in terms of confidence. Finally, a statistically significant association of a positive sign was identified between the use of social media as the main source and the acceptance of misinformation.info:eu-repo/semantics/publishedVersio
Reciprocity, Homophily, and Social Network Effects in Pictorial Communication: A Case Study of Bitmoji Stickers
Pictorial emojis and stickers are commonly used in online social networking
to facilitate and aid communications. We delve into the use of Bitmoji
stickers, a highly expressive form of pictorial communication using avatars
resembling actual users. We collect a large-scale dataset of the metadata of 3
billion Bitmoji stickers shared among 300 million Snapchat users. We find that
individual Bitmoji sticker usage patterns can be characterized jointly on
dimensions of reciprocity and selectivity: Users are either both reciprocal and
selective about whom they use Bitmoji stickers with or neither reciprocal nor
selective. We additionally provide evidence of network homophily in that
friends use Bitmoji stickers at similar rates. Finally, using a
quasi-experimental approach, we show that receiving Bitmoji stickers from a
friend encourages future Bitmoji sticker usage and overall Snapchat engagement.
We discuss broader implications of our work towards a better understanding of
pictorial communication behaviors in social networks.Comment: 21 page
Family Factors And Perceived Coworker Support And Supervisor Support
This study examines how aspects of family formation relate to coworker support and supervisor support. Studying both coworker support and supervisor support is valuable because they can give us a glimpse of how different people feel about the workplace. Using the theoretical perspective of homophily, which focuses on how people prefer to interact with others who are similar to themselves, it was hypothesized that people who are married or who have children will perceive more coworker support and supervisor support than others. The data set of the 2002 National Study of the Changing Workforce was used. It contained 3,368 cases for the analysis of coworker support and 2,506 cases for the analysis of supervisor support. Findings suggest that people who are married do perceive more coworker support than people who are not married, but parental status was unrelated to coworker support. It was also found that marital status and parental status were unconnected to supervisor support. Implications of these findings are discussed
What are Your Pronouns? Examining Gender Pronoun Usage on Twitter
Stating your gender pronouns, along with your name, is becoming the new norm
of self-introductions at school, at the workplace, and online. The increasing
prevalence and awareness of nonconforming gender identities put discussions of
developing gender-inclusive language at the forefront. This work presents the
first empirical research on gender pronoun usage on large-scale social media.
Leveraging a Twitter dataset of over 2 billion tweets collected continuously
over two years, we find that the public declaration of gender pronouns is on
the rise, with most people declaring as using she series pronouns, followed by
he series pronouns, and a smaller but considerable amount of non-binary
pronouns. From analyzing Twitter posts and sharing activities, we can discern
users who use gender pronouns from those who do not and also distinguish users
of various gender identities. We further illustrate the relationship between
explicit forms of social network exposure to gender pronouns and their eventual
gender pronoun adoption. This work carries crucial implications for
gender-identity studies and initiates new research directions in gender-related
fairness and inclusion, as well as support against online harassment and
discrimination on social media.Comment: 23 pages, 11 figures, 2 table
Confidence-Based Feature Imputation for Graphs with Partially Known Features
This paper investigates a missing feature imputation problem for graph
learning tasks. Several methods have previously addressed learning tasks on
graphs with missing features. However, in cases of high rates of missing
features, they were unable to avoid significant performance degradation. To
overcome this limitation, we introduce a novel concept of channel-wise
confidence in a node feature, which is assigned to each imputed channel feature
of a node for reflecting certainty of the imputation. We then design
pseudo-confidence using the channel-wise shortest path distance between a
missing-feature node and its nearest known-feature node to replace unavailable
true confidence in an actual learning process. Based on the pseudo-confidence,
we propose a novel feature imputation scheme that performs channel-wise
inter-node diffusion and node-wise inter-channel propagation. The scheme can
endure even at an exceedingly high missing rate (e.g., 99.5\%) and it achieves
state-of-the-art accuracy for both semi-supervised node classification and link
prediction on various datasets containing a high rate of missing features.
Codes are available at https://github.com/daehoum1/pcfi.Comment: Accepted to ICLR 2023. 28 page
A trend study on the impact of social media on advertisement
This paper presents a comprehensive scientometric study for the impact of social networks on advertisement. The study uses the Scopus database as a search engine to accomplish the survey. To better understand the evolution and identity of this category, the study covers 1216 most cited data over the period 1983-2019. Qualitative and quantitative data analysis techniques are applied to determine author distribution, country, individual and institutional-level productivity rankings. In terms of keywords, the study indicates that social media was jointly studied with gender and be-havior and researchers from the United States maintained the highest rate of contribution. The survey also indicates that there were strong collaboration between the researchers from China and United States. Moreover, there were also remarkable collaborations between the researchers in United States from one side and other countries