The collaborative search by tag-based user profile in social media.
Ontology highlight
ABSTRACT: Recently, we have witnessed the popularity and proliferation of social media applications (e.g., Delicious, Flickr, and YouTube) in the web 2.0 era. The rapid growth of user-generated data results in the problem of information overload to users. Facing such a tremendous volume of data, it is a big challenge to assist the users to find their desired data. To attack this critical problem, we propose the collaborative search approach in this paper. The core idea is that similar users may have common interests so as to help users to find their demanded data. Similar research has been conducted on the user log analysis in web search. However, the rapid growth and change of user-generated data in social media require us to discover a brand-new approach to address the unsolved issues (e.g., how to profile users, how to measure the similar users, and how to depict user-generated resources) rather than adopting existing method from web search. Therefore, we investigate various metrics to identify the similar users (user community). Moreover, we conduct the experiment on two real-life data sets by comparing the Collaborative method with the latest baselines. The empirical results show the effectiveness of the proposed approach and validate our observations.
Project description:Privacy-preserving profile matching, a challenging task in mobile social networks, is getting more attention in recent years. In this paper, we propose a novel scheme that is based on ciphertext-policy attribute-based encryption to tackle this problem. In our scheme, a user can submit a preference-profile and search for users with matching-profile in decentralized mobile social networks. In this process, no participant's profile and the submitted preference-profile is exposed. Meanwhile, a secure communication channel can be established between the pair of successfully matched users. In contrast to existing related schemes which are mainly based on the secure multi-party computation, our scheme can provide verifiability (both the initiator and any unmatched user cannot cheat each other to pretend to be matched), and requires few interactions among users. We provide thorough security analysis and performance evaluation on our scheme, and show its advantages in terms of security, efficiency and usability over state-of-the-art schemes.
Project description:The rapid development of short-video social network platforms provides us with anopportunity to conduct health-related advertising and recommendation. However, so far, there areno empirical evidence on whether users are willing to accept health-related short-videoadvertisements. Here, acceptance refers to purchase intention, meaning that users will read shortvideoads, share ads with others, or even open the product link embedded in ads to purchase theproduct. In this paper, we make the first attempt to model and quantify user acceptance of healthrelatedshort-video advertisements. Particularly, we propose a new research model that enhancesthe Technology Acceptance Model (TAM) with two new designs. First, we propose four newantecedents including social interaction, intrusiveness, informativeness, and relevance into theoriginal TAM to reflect the features of short-video social networks. Second, we introduce twomediator variables including perceived usefulness and attitude so that we can better study howdifferent factors affect user acceptance of health-related short-video ads. We perform a survey onthe Internet and conduct an empirical analysis of the surveyed data. The results show that the fourantecedents as well as the perceived ease of use have significant influences on perceived usefulness,attitude, and purchase intention. Further, perceived usefulness plays a valid mediating role inattitude and purchase intention. We also found that users' perceived ease of use on health-relatedshort-video ads cannot significantly predict users' attitudes toward ads. This is a new finding insocial media-oriented ads. Finally, we integrate the empirical findings and present reasonablesuggestions for advertisers and marketers to promote health-related short-video ads.
Project description:BackgroundThe number of electronic cigarette (e-cigarette) users has been increasing rapidly in recent years, especially among youth and young adults. More e-cigarette products have become available, including e-liquids with various brands and flavors. Various e-liquid flavors have been frequently discussed by e-cigarette users on social media.ObjectiveThis study aimed to examine the longitudinal prevalence of mentions of electronic cigarette liquid (e-liquid) flavors and user perceptions on social media.MethodsWe applied a data-driven approach to analyze the trends and macro-level user sentiments of different e-cigarette flavors on social media. With data collected from web-based stores, e-liquid flavors were classified into categories in a flavor hierarchy based on their ingredients. The e-cigarette-related posts were collected from social media platforms, including Reddit and Twitter, using e-cigarette-related keywords. The temporal trend of mentions of e-liquid flavor categories was compiled using Reddit data from January 2013 to April 2019. Twitter data were analyzed using a sentiment analysis from May to August 2019 to explore the opinions of e-cigarette users toward each flavor category.ResultsMore than 1000 e-liquid flavors were classified into 7 major flavor categories. The fruit and sweets categories were the 2 most frequently discussed e-liquid flavors on Reddit, contributing to approximately 58% and 15%, respectively, of all flavor-related posts. We showed that mentions of the fruit flavor category had a steady overall upward trend compared with other flavor categories that did not show much change over time. Results from the sentiment analysis demonstrated that most e-liquid flavor categories had significant positive sentiments, except for the beverage and tobacco categories.ConclusionsThe most updated information about the popular e-liquid flavors mentioned on social media was investigated, which showed that the prevalence of mentions of e-liquid flavors and user perceptions on social media were different. Fruit was the most frequently discussed flavor category on social media. Our study provides valuable information for future regulation of flavored e-cigarettes.
Project description:Social media ranking algorithms typically optimize for users' revealed preferences, i.e. user engagement such as clicks, shares, and likes. Many have hypothesized that by focusing on users' revealed preferences, these algorithms may exacerbate human behavioral biases. In a preregistered algorithmic audit, we found that, relative to a reverse-chronological baseline, Twitter's engagement-based ranking algorithm amplifies emotionally charged, out-group hostile content that users say makes them feel worse about their political out-group. Furthermore, we find that users do not prefer the political tweets selected by the algorithm, suggesting that the engagement-based algorithm underperforms in satisfying users' stated preferences. Finally, we explore the implications of an alternative approach that ranks content based on users' stated preferences and find a reduction in angry, partisan, and out-group hostile content, but also a potential reinforcement of proattitudinal content. Overall, our findings suggest that greater integration of stated preferences into social media ranking algorithms could promote better online discourse, though potential trade-offs also warrant further investigation.
Project description:Contrary to common intuition, a group of people recalling information together remembers less than the same number of individuals recalling alone (i.e., the collaborative inhibition effect). To understand this effect in a free recall task, we build a computational model of collaborative recall in groups, extended from the Context Maintenance and Retrieval (CMR) model, which captures how individuals recall information alone. We propose that in collaborative recall, one not only uses their previous recall as an internal retrieval cue, but one also listens to someone else's recall and uses it as an external retrieval cue. Attending to this cue updates the listener's context to be more similar to the context of someone else's recall. Over an existing dataset of individual and collaborative recall in small and large groups, we show that our model successfully captures the difference in memory performance between individual recall and collaborative recall across different group sizes from 2 to 16, as well as additional recall patterns such as recency effects and semantic clustering effects. Our model further shows that collaborating individuals reach similar areas in the context space, whereby their contexts converge more than the contexts of individuals recalling alone. This convergence constrains their ability to search memories effectively and is negatively associated with recall performance. We discuss the contributions of our modeling results in relation to previous accounts of the collaborative inhibition effect.
Project description:The science on controversial topics is often heatedly discussed on social media, a potential problem for social-media-based science communicators. Therefore, two exploratory studies were performed to investigate the effects of science-critical user comments attacking Facebook posts containing scientific claims. The claims were about one of four controversial topics (homeopathy, genetically modified organisms, refugee crime, and childhood vaccinations). The user comments attacked the claims based on the thematic complexity, the employed research methods, the expertise, or the motivations of the researchers. The results reveal that prior attitudes determine judgments about the user comments, the attacked claims, and the source of the claim. After controlling for attitude, people agree most with thematic complexity comments, but the comments differ in their effect on perceived claim credibility only when the comments are made by experts. In addition, comments attacking researchers' motivations were more effective in lowering perceived integrity while scientists' perceived expertise remained unaffected.
Project description:Urban parks and green spaces are among the few places where city dwellers can have regular contact with nature and engage in outdoor recreation. Social media data provide opportunities to understand such human-environment interactions. While studies have demonstrated that geo-located photographs are useful indicators of recreation across different spaces, recreation behaviour also varies between different groups of people. Our study used social media to assess behavioural patterns across different groups of park users in tropical Singapore. 4,674 users were grouped based on the location and content of their photographs on the Flickr platform. We analysed how these groups varied spatially in the parks they visited, as well as in their photography behaviour. Over 250,000 photographs were analysed, including those uploaded and favourited by users, and all photographs taken at city parks. There were significant differences in the number and types of park photographs between tourists and locals, and between user-group axes formed from users' photograph content. Spatial mapping of different user groups showed distinct patterns in the parks they were attracted to. Future work should consider such variability both within and between data sources, to provide a more context-dependent understanding of human-environment interactions and preferences for outdoor recreation.
Project description:The exploration of social media comment analysis has garnered considerable scholarly attention in recent epochs, precipitated by the pervasive ubiquity of social media platforms and the copious volume of commentaries engendered by their users. As the prevalence of users disseminating opinions, engaging in news discourse, and articulating sentiments on social media escalates, scrutinizing social media comments assumes paramount significance. This treatise employs a sophisticated deep network model for sentiment classification predicated on online social media textual commentary data, utilizing a bidirectional long short-term memory (BI-LSTM) network. The model initiates data input processing by employing word segmentation and word vector extraction, culminating in the formation of an attention-based bidirectional long short-term memory (ATT-Bi-LSTM) model, which incorporates an attention mechanism for discerning positive and negative emotions. Notably, the model attains recognition rates exceeding 80% for both categories of emotions within the public dataset. Concurrently, the model undergoes training migration for practical application validation using the public dataset, yielding recognition accuracy surpassing 90% in authentic testing scenarios. This substantiates the efficacy of the proposed methodology in proficiently accomplishing the emotion classification task within the dynamic text milieu of social media news propagation. Such proficiency, in turn, furnishes pivotal technical underpinnings for subsequent iterations of intelligent push models and astute public opinion analyses.
Project description:Searching similar pictures for a given picture is an important task in numerous applications, including image recommendation system, image classification and image retrieval. Previous studies mainly focused on the similarities of content, which measures similarities based on visual features, such as color and shape, and few of them pay enough attention to semantics. In this paper, we propose a link-based semantic similarity search method, namely PictureSim, for effectively searching similar pictures by building a picture-tag network. The picture-tag network is built by "description" relationships between pictures and tags, in which tags and pictures are treated as nodes, and relationships between pictures and tags are regarded as edges. Then we design a TF-IDF-based model to removes the noisy links, so the traverses of these links can be reduced. We observe that "similar pictures contain similar tags, and similar tags describe similar pictures", which is consistent with the intuition of the SimRank. Consequently, we utilize the SimRank algorithm to compute the similarity scores between pictures. Compared with content-based methods, PictureSim could effectively search similar pictures semantically. Extensive experiments on real datasets to demonstrate the effectiveness and efficiency of the PictureSim.
Project description:BackgroundThe high prevalence of noncommunicable diseases and the growing importance of social media have prompted health care professionals (HCPs) to use social media to deliver health information aimed at reducing lifestyle risk factors. Previous studies have acknowledged that the identification of elements that influence user engagement metrics could help HCPs in creating engaging posts toward effective health promotion on social media. Nevertheless, few studies have attempted to comprehensively identify a list of elements in social media posts that could influence user engagement metrics.ObjectiveThis systematic review aimed to identify elements influencing user engagement metrics in social media posts by HCPs aimed to reduce lifestyle risk factors.MethodsRelevant studies in English, published between January 2006 and June 2023 were identified from MEDLINE or OVID, Scopus, Web of Science, and CINAHL databases. Included studies were those that examined social media posts by HCPs aimed at reducing the 4 key lifestyle risk factors. Additionally, the studies also outlined elements in social media posts that influenced user engagement metrics. The titles, abstracts, and full papers were screened and reviewed for eligibility. Following data extraction, narrative synthesis was performed. All investigated elements in the included studies were categorized. The elements in social media posts that influenced user engagement metrics were identified.ResultsA total of 19 studies were included in this review. Investigated elements were grouped into 9 categories, with 35 elements found to influence user engagement. The 3 predominant categories of elements influencing user engagement were communication using supportive or emotive elements, communication aimed toward behavioral changes, and the appearance of posts. In contrast, the source of post content, social media platform, and timing of post had less than 3 studies with elements influencing user engagement.ConclusionsFindings demonstrated that supportive or emotive communication toward behavioral changes and post appearance could increase postlevel interactions, indicating a favorable response from the users toward posts made by HCPs. As social media continues to evolve, these elements should be constantly evaluated through further research.