You are presented with multiple paper abstracts, alongside the associated literature review and chapter titles. Given the provided information, you are going to do a literature review. Please generate a coherent literature review that synthesizes the content, draws connections between different works, highlights major findings, and identifies any gaps in the existing research.

Literature Review Title: A Survey on Mobile Crowd-Sensing and Its Applications in the IoT Era 
Chapter Title:  E. SOCIAL NETWORKING 
1. Abstract of Cited Paper (BIB001):  Mobile phone sensing is an emerging area of interest for researchers as smart phones are becoming the core communication device in people's everyday lives. Sensor enabled mobile phones or smart phones are hovering to be at the center of a next revolution in social networks, green applications, global environmental monitoring, personal and community healthcare, sensor augmented gaming, virtual reality and smart transportation systems. More and more organizations and people are discovering how mobile phones can be used for social impact, including how to use mobile technology for environmental protection, sensing, and to leverage just-in-time information to make our movements and actions more environmentally friendly. In this paper we have described comprehensively all those systems which are using smart phones and mobile phone sensors for humans good will and better human phone interaction. 
2. Abstract of Cited Paper (BIB002):  In this paper we focus on the connection between age and language use, exploring age prediction of Twitter users based on their tweets. We discuss the construction of a fine-grained annotation effort to assign ages and life stages to Twitter users. Using this dataset, we explore age prediction in three different ways: classifying users into age categories, by life stages, and predicting their exact age. We find that an automatic system achieves better performance than humans on these tasks and that both humans and the automatic systems have difficulties predicting the age of older people. Moreover, we present a detailed analysis of variables that change with age. We find strong patterns of change, and that most changes occur at young ages. 
3. Abstract of Cited Paper (BIB003):  This article describes LineKing, a crowdsensing system for monitoring and forecasting coffee shop line wait times. LineKing consists of a smartphone component that provides automatic and accurate wait-time detection, and a cloud backend that uses the collected data to provide accurate wait-time estimation. LineKing is used on a daily basis by hundreds of users to monitor the wait-times of a coffee shop in the University at Buffalo, SUNY. The novel wait-time estimation algorithms of LineKing deployed at the cloud backend provide median absolute errors of less than 3 minutes. 
4. Abstract of Cited Paper (BIB004):  Community bulletin boards serve an important function for public information sharing in modern society. Posted fliers advertise services, events, and other announcements. However, fliers posted offline suffer from problems such as limited spatial-temporal coverage and inefficient search support. In recent years, with the development of sensor-enhanced mobile devices, mobile crowd sensing (MCS) has been used in a variety of application areas. This paper presents FlierMeet, a crowd- powered sensing system for cross-space public information reposting, tagging, and sharing. The tags learned are useful for flier sharing and preferred information retrieval and suggestion. Specifically, we utilize various contexts (e.g., spatio-temporal info, flier publishing/reposting behaviors, etc.) and textual features to group similar reposts and classify them into categories. We further identify a novel set of crowd-object interaction hints to predict the semantic tags of reposts. To evaluate our system, 38 participants were recruited and 2,035 reposts were captured during an eight-week period. Experiments on this dataset showed that our approach to flier grouping is effective and the proposed features are useful for flier category/semantic tagging. 
5. Abstract of Cited Paper (BIB005):  This paper presents a group-aware mobile crowd sensing system called MobiGroup, which supports group activity organization in real-world settings. Acknowledging the complexity and diversity of group activities, this paper introduces a formal concept model to characterize group activities and classifies them into four organizational stages. We then present an intelligent approach to support group activity preparation, including a heuristic rule-based mechanism for advertising public activity and a context-based method for private group formation. In addition, we leverage features extracted from both online and offline communities to recommend ongoing events to attendees with different needs. Compared with the baseline method, people preferred public activities suggested by our heuristic rule-based method. Using a dataset collected from 45 participants, we found that the context-based approach for private group formation can attain a precision and recall of over 80%, and the usage of spatial–temporal contexts and group computing can have more than a 30% performance improvement over considering the interaction frequency between a user and related groups. A case study revealed that, by extracting the features such as dynamic intimacy and static intimacy, our cross-community approach for ongoing event recommendation can meet different user needs. 
6. Abstract of Cited Paper (BIB006):  AbstractThe use of standardized test results to drive school administrator evaluations pervades education policymaking in more than 40 states. However, the results of state standardized tests are strongly influenced by non-school factors. The models of best fit (n = 18) from this correlational, explanatory, longitudinal study predicted accurately the percentage of middle school students scoring proficient or above on the New Jersey state-mandated standardized tests in mathematics and language arts for grades 6–8 during the years 2010, 2011, and 2012 for 70% to 78% of the schools in the statewide samples (n = 292 to 311), using only family and community demographic variables from the U.S. Census. Just three demographic variables, (a) percentage of families in a community with income over $200,000 a year, (b) percentage of people in a community in poverty, and (c) percentage of people in a community with bachelor’s degrees, predicted results accurately in 14/18 of the models. The findings suggest that state... 
7. Abstract of Cited Paper (BIB007):  Community question answering services (CQAS) (e.g., Yahoo! Answers) provides a platform where people post questions and answer questions posed by others. Previous works analyzed the answer quality (AQ) based on answer-related features, but neglect the question-related features on AQ. Previous work analyzed how asker- and question-related features affect the question quality (QQ) regarding the amount of attention from users, the number of answers and the question solving latency, but neglect the correlation between QQ and AQ (measured by the rating of the best answer), which is critical to quality of service (QoS). We handle this problem from two aspects. First, we additionally use QQ in measuring AQ, and analyze the correlation between a comprehensive list of features (including answer-related features) and QQ. Second, we propose the first method that estimates the probability for a given question to obtain high AQ. Our analysis on the Yahoo! Answers trace confirmed that the list of our identified features exert influence on AQ, which determines QQ. For the correlation analysis, the previous classification algorithms cannot consider the mutual interactions between multiple ( $>$ 2) classes of features. We then propose a novel Coupled Semi-Supervised Mutual Reinforcement-based Label Propagation (CSMRLP) algorithm for this purpose. Our extensive experiments show that CSMRLP outperforms the Mutual Reinforcement-based Label Propagation (MRLP) and five other traditional classification algorithms in the accuracy of AQ classification, and the effectiveness of our proposed method in AQ prediction. Finally, we provide suggestions on how to create a question that will receive high AQ, which can be exploited to improve the QoS of CQAS.

Literature Review:
Social can be categorized into two categories: social network and social sensing information. In social network, the users can share their information between each other by utilizing many systems like LinkedIn, Twitter, Facebook, and Yahoo! Answers BIB008 . A large number of individuals connected in a social network can deliver best answers to complex problems as compared to a single individual. While in social sensing, the MCS application collects data about personal activity and sends it to the remote server for further processing. In such system, the users can share their information only among certain groups of friends or community for privacy purposes. Since most of the data are sensitive information such as personal health, location, pictures and videos; the community approach is the best one to collect and integrate data from several people BIB002 Morgan-Lopez et al.  examine the single and joint predictive validity of linguistic and metadata features in predicting the age of Twitter users. Authors created a dataset that describes Twitter users across several groups of ages such as youth, young adult, and adults, then collecting their birthday. Finally, examine the predictive validity of the following features: language only, metadata only, language and metadata, and phrases from other age-validated dataset. The result suggested that examining linguistic and Twitter metadata features for prediction may be helpful for public health and evaluation research. In BIB006 , an application called MobiGroup, combining mobile computing with social networks, is a group-aware system that delivers assistance during several group activity organizational stages. MobiGroup is a smartphone sensing system to endorse current activities based on user activity distance and interaction dynamics in a community. Also, it uses smartphone sensing to capture online/offline social events and enables group formation and management. Bulut et al. BIB004 present a crowd-sensing application for observing and predicting the waiting time to enter a coffee shop called LineKing. LineKing is used on daily basis to monitor and estimate the wait time for hundreds of users at a coffee shop in the University at Buffalo, SUNY. In addition, it uses uninterrupted streams of accelerometer data provided by participants to detect the waiting time of users. Another MCS application is FlierMeet BIB005 that applies crowdpowered sensing system to collect and share public information in cross-space using the built-in sensors of smart mobile phones. Authors utilize several contexts (e.g., flier publishing/reposting behaviors, spatio-temporal info, etc.) and written features to group and categorize related reposts. FlierMeet application captures the data through 38 recruited participants and 2,035 reposts during eight-weeks. The results proved that FlierMeet is an effective and convenient application for flier category tagging. Another interesting discussion is how to correlate information extracted from smartphone data with other information from demographics or socio-economical status to predict and anticipate better results for the individual user. The study of the population based on several factors such as race, gender, age, education, income, marital status, job, religion, birth rate, death rate,.., etc. is called demographics. Frequently, demographical studies are used by governments, corporations, and non-governments to learn more about population characteristics for several purposes, plus policy development and economic market research BIB007 . The factors of family demographics help in describing the level of human capital in a family. In  the work assesses the separate and common predictive of lingual authenticity and metadata approaches to predict ages. The work was based on both Al Zamal et al. BIB001 and Nguyen et al. BIB003 research work which inspects the common prediction of annotation age and labeling. Their objective was to increase the validaty of age prediction in Twitter.