The Role of Language in Structuring Social Networks Following Market Integration in a Yucatec Maya Population

Language is the human universal mode of communication, and is dynamic and constantly in flux accommodating user needs as individuals interface with a changing world. However, we know surprisingly little about how language responds to market integration, a pressing force affecting indigenous communities worldwide today. While models of culture change often emphasize the replacement of one language, trait, or phenomenon with another following socioeconomic transitions, we present a more nuanced framework. We use demographic, economic, linguistic, and social network data from a rural Maya community that spans a 27-year period and the transition to market integration. By adopting this multivariate approach for the acquisition and use of languages, we find that while the number of bilingual speakers has significantly increased over time, bilingualism appears stable rather than transitionary. We provide evidence that when indigenous and majority languages provide complementary social and economic payoffs, both can be maintained. Our results predict the circumstances under which indigenous language use may be sustained or at risk. More broadly, the results point to the evolutionary dynamics that shaped the current distribution of the world’s linguistic diversity.

Publishing Triangle Counting Histogram in Social Networks Based on Differential Privacy

The continuous expansion of the number and scale of social networking sites has led to an explosive growth of social network data. Mining and analyzing social network data can bring huge economic value and social benefits, but it will result in privacy leakage and other issues. The research focus of social network data publishing is to publish available data while ensuring privacy. Aiming at the problem of low data availability of social network node triangle counting publishing under differential privacy, this paper proposes a privacy protection method of edge triangle counting. First, an edge-removal projection algorithm TSER based on edge triangle count sorting is proposed to obtain the upper bound of sensitivity. Then, two edge triangle count histogram publishing methods satisfying edge difference privacy are given based on the TSER algorithm. Finally, experimental results show that compared with the existing algorithms, the TSER algorithm can retain more triangles in the original graph, reduce the error between the published data and the original data, and improve the published data availability.

Healthy Lifestyle and Social Network Prolong Disability-Free Survival in Older Adults With Diabetes

Aim We aimed to estimate the extent to which diabetes shortens disability-free survival, and identify which factors may prolong disability-free survival in older adults with diabetes. Methods A total of 2,216 disability-free participants aged ≥60 were followed up to 15 years. Diabetes was ascertained through antidiabetic drug use, medical records, or HbA1c ≥ 6.5%. Disability-free survival was defined as the survival until the occurrence of disability. Data on behaviours (healthy vs. unhealthy), leisure activities (active vs. inactive), and social network (moderate-to-rich vs. poor) were collected at baseline. A favourable (vs. unfavourable) lifestyle profile was defined as the presence of at least one of healthy behaviours, active engagement in leisure activities, and/or moderate-to-rich social network. Data were analysed with Cox regression and Laplace regression. Results During the follow-up, 1,345 (60.7%) participants developed disability/death. Diabetes was related to the outcome (HR 1.29, 95% CI 1.06–1.57), and shortened 2.15 (1.02–3.27) years of median disability-free survival. Additionally, disability-free survival (95% CI) was shortened by 3.29 (1.21–5.36), 3.92 (2.08–5.76) and 1.66 (0.06–3.28) years for participants with diabetes plus unhealthy behaviours, inactive leisure activities, or poor social network, respectively (reference: no diabetes plus healthy behaviours, leisure activities, or moderate-to-rich social network). Among participants with diabetes, a favourable profile led to a non-significant HR of 1.19 (0.93–1.56) for disability/death and prolonged disability-free survival by 3.26 (2.33–4.18) years than those with unfavourable profile. Conclusions Healthy lifestyle and/or moderate-to-rich social network attenuates the risk of diabetes on disability/death and prolongs disability-free survival in people with diabetes by 3 years.

The Geography of Social Media Data in Urban Areas: Representativeness and Complementarity

This research sheds light on the relationship between the presence of location-based social network (LBSN) data and other economic and demographic variables in the city of Valencia (Spain). For that purpose, a comparison is made between location patterns of geolocated data from various social networks (i.e., Google Places, Foursquare, Twitter, Airbnb and Idealista) and statistical information such as land value, average gross income, and population distribution by age range. The main findings show that there is no direct relationship between land value or age of registered population and the amount of social network data generated in a given area. However, a noteworthy coincidence was observed between Google Places data-clustering patterns, which represent the offer of economic activities, and the spatial concentration of the other LBSNs analyzed, suggesting that data from these sources are mostly generated in areas with a high density of economic activities.

Detecting Bots and Assessing Their Impact in Social Networks

Bots Impact Opinions in Social Networks: Let’s Measure How Much There is a serious threat posed by bots that try to manipulate opinions in social networks. In “Assessing the Impact of Bots on Social Networks,” Nicolas Guenon des Mesnards, David Scott Hunter, Zakaria el Hjouiji, and Tauhid Zaman present a new set of operational capabilities to detect these bots and measure their impact. They developed an algorithm based on the Ising model from statistical physics to find coordinating gangs of bots in social networks. They then created an algorithm based on opinion dynamics models to quantify the impact that bots have on opinions in a social network. They applied their algorithms to a variety of real social network data sets. They found that, for topics such as Brexit, the bots had little impact, whereas for topics such as the U.S. presidential debate and the Gilets Jaunes protests in France, the bots had a significant impact.

Image Based Classification of Rumor Information from the Social Network Platform

Spam emails based on images readily evade text-based spam email filters. More and more spammers are adopting the technology. The essence of email is necessary in order to recognize image content. Web-based social networking is a method of communication between the information owner and end users for online exchanges that use social network data in the form of images and text. Nowadays, information is passed on to users in shorter time using social networks, and the spread of fraudulent material on social networks has become a major issue. It is critical to assess and decide which features the filters require to combat spammers. Spammers also insert text into photographs, causing text filters to fail. The detection of visual garbage material has become a hotspot study on spam filters on the Internet. The suggested approach includes a supplementary detection engine that uses visuals as well as text input. This paper proposed a system for the assessment of information, the detection of information on fraud-based mails and the avoidance of distribution to end users for the purpose of enhancing data protection and preventing safety problems. The proposed model utilizes Machine Learning and Convolutional Neural Network (CNN) methods to recognize and prevent fraud information being transmitted to end users.