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In recent years, the establishment of a substantial amount of academic groups on scientific social network has brought new opportunities for the collaboration among researchers. In this situation, conducting paper recommendation to these academic groups is of terrific necessity in that it can further facilitate group activities. However, when producing group recommendation, existing methods fail to make full use of the abundant group information, from which a great deal of valuable information can be inferred to facilitate the recommendation performance. In addition, those methods tend to assign an equal weight to each group member when aggregating their recommendations, which is unreasonable in practice. Although some improvements have been made to remedy this problem by assigning different weights to group members, they fail to take into account the reliabilities of group members. Therefore, a group-oriented paper recommendation method based on probabilistic matrix factorization and evidential reasoning (GPMF_ER) is proposed in this article to tackle these problems. More specifically, the group and paper content information are integrated into the probabilistic matrix factorization model to enhance the accuracy of individual recommendation. Afterward, evidential reasoning rule is introduced in the aggregation step to consider both the weights and reliabilities of group members. Extensive experiments have been conducted on the real world CiteULike dataset and the results demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Many datasets including social media data and bibliographic data can be modeled as graphs. Clustering such graphs is able to provide useful insights into the structure of the data. To improve the quality of clustering, node attributes can be taken into account, resulting in attributed graphs. Existing attributed graph clustering methods generally consider attribute similarity and structural similarity separately. In this paper, we represent attributed graphs as star-schema heterogeneous graphs, where attributes are modeled as different types of graph nodes. This enables the use of personalized pagerank (PPR) as a unified distance measure that captures both structural and attribute similarities. We employ DBSCAN for clustering, and we update edge weights iteratively to balance the importance of different attributes. The rapidly growing volume of data nowadays challenges traditional clustering algorithms, and thus, a distributed method is required. Hence, we adopt a popular distributed graph computing system Blogel, based on which, we develop four exact and approximate approaches that enable efficient PPR score computation when edge weights are updated. To improve the effectiveness of the clustering, we propose a simple yet effective edge weight update strategy based on entropy. In addition, we present a game theory based method that enables trading efficiency for result quality. Extensive experiments on real-life datasets offer insights into the effectiveness and efficiency of our proposals. [ABSTRACT FROM AUTHOR]

The ever-evolving social media discourse has witnessed an overwhelming use of memes to express opinions or dissent. Besides being misused for spreading malcontent, they are mined by corporations and political parties to glean the public's opinion. Therefore, memes predominantly offer affect-enriched insights towards ascertaining the societal psyche. However, the current approaches are yet to model the affective dimensions expressed in memes effectively. They rely extensively on large multimodal datasets for pre-training and do not generalize well due to constrained visual-linguistic grounding. In this paper, we introduce MOOD (Meme emOtiOns Dataset), which embodies six basic emotions. We then present ALFRED (emotion-Aware muLtimodal Fusion foR Emotion Detection), a novel multimodal neural framework that (i) explicitly models emotion-enriched visual cues, and (ii) employs an efficient cross-modal fusion via a gating mechanism. Our investigation establishes ALFRED's superiority over existing baselines by 4.94% F1. Additionally, ALFRED competes strongly with previous best approaches on the challenging Memotion task. We then discuss ALFRED's domain-agnostic generalizability by demonstrating its dominance on two recently-released datasets – HarMeme and Dank Memes, over other baselines. Further, we analyze ALFRED's interpretability using attention maps. Finally, we highlight the inherent challenges posed by the complex interplay of disparate modality-specific cues toward meme analysis. [ABSTRACT FROM AUTHOR]

Presents the table of contents for this issue of the publication. [ABSTRACT FROM PUBLISHER]

Yahoo! Answers is an online platform where users can post questions and answer other users’ questions. Our previous work studied the online social network (OSN) of Yahoo! Answers by analyzing information from the profiles (including fans, contacts, and interests) of top contributors and their related users. Rather than using the static profile information from the top-contributor-centered dataset, in this paper, we particularly analyze the actual questioning and answering (Q/A) behaviors of normal users. We build a Q/A network that unidirectionally connects each asker to his/her answerers. We analyze the structural characteristics of the Q/A network, user Q/A activities, and knowledge base of all users. In addition to the observations similar to our previous study, which indicates that the OSN of Yahoo! Answers can reflect user Q/A activities to a certain extent, we additionally observe that: 1) a large portion of users only ask questions without answering others’ questions; 2) users are active in more knowledge categories than those indicated in their profiles; and 3) the knowledge categories of the top-contributor-related users cannot represent those of normal users. Finally, we analyze the characteristics of questions and answers in different knowledge categories. This paper not only provides an understanding of actual Q/A activities of users but also showcases the aspects of Q/A activities that the OSN of Yahoo! Answers can and cannot accurately reflect. Based on the insights gained from this paper, we propose a few methods to help improve the quality of service of Yahoo! Answers. [ABSTRACT FROM PUBLISHER]

To celebrate the achievements of the magazine and to recognize the profound contributions from authors, IEEE MultiMedia has establish two annual awards starting this year--best article and best department. Learn about the first winners of these two new awards. [ABSTRACT FROM PUBLISHER]

With the popularity of news on social media, fake news has become an important issue for the public and government. There exist some fake news detection methods that focus on information exploration and utilization from multiple modalities, e.g., text and image. However, how to effectively learn both modality-invariant and event-invariant discriminant features is still a challenge. In this paper, we propose a novel approach named Modality and Event Adversarial Networks (MEAN) for fake news detection. It contains two parts: a multi-modal generator and a dual discriminator. The multi-modal generator extracts latent discriminant feature representations of text and image modalities. A decoder is adopted to reduce information loss in the generation process for each modality. The dual discriminator includes a modality discriminator and an event discriminator. The discriminator learns to classify the event or the modality of features, and network training is guided by the adversarial scheme. Experiments on two widely used datasets show that MEAN can perform better than state-of-the-art related multi-modal fake news detection methods. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has affected the whole world since the beginning of 2020. In Brazil, over 70% of the population rely on the Brazil's Unified Health System (SUS). Knowing public opinion related to SUS is very important for the improvement of services and assistance provided by such an entity. Sentiment analysis has been used in several applications including social networks and blogs to extract public opinion. Despite the fact that other papers have already worked with sentiment analysis, none of them have focused on SUS. Therefore, the goal of this paper is to analyse the sentiments shown by Brazillian Twitter users about SUS before and during COVID-19 pandemic. To reach this goal, a database of portuguese tweets regarding SUS posted between december 2019 and october 2020 was created. The tweets were pre-processed, classified and then analysed. The results show that, in most cases, users are in favor of SUS. [ABSTRACT FROM AUTHOR]

Subway passenger flow prediction is strategically important in metro transit system management. The prediction under event occurrences turns into a very challenging task. In this paper, we adopt a new kind of data source—social media—to tackle this challenge. We develop a systematic approach to examine social media activities and sense event occurrences. Our initial analysis demonstrates that there exists a moderate positive correlation between passenger flow and the rates of social media posts. This finding motivates us to develop a novel approach for improved flow forecast. We first develop a hashtag-based event detection algorithm. Furthermore, we propose a parametric and convex optimization-based approach, called optimization and prediction with hybrid loss function (OPL), to fuse the linear regression and the results of seasonal autoregressive integrated moving average (SARIMA) model jointly. The OPL hybrid model takes advantage of the unique strengths of linear correlation in social media features and SARIMA model in time series prediction. Experiments on events nearby a subway station show that OPL reports the best forecasting performance compared with other state-of-the-art techniques. In addition, an ensemble model is developed to leverage the weighted results from OPL and support vector machine regression together. As a result, the prediction accuracy and the robustness further increase. [ABSTRACT FROM PUBLISHER]

This paper deals with change detection of utility maximization behavior given a dataset. In online social media, such changes occur due to the effect of marketing, advertising, or changes in ground truth. First, we use the revealed preference framework to detect the unknown time point (change point) at which the utility function changed. We derive necessary and sufficient conditions for detecting the change point. Second, in the presence of noisy measurements, we propose a method to detect the change point and construct a decision test. Also, an optimization criteria is provided to recover the linear perturbation coefficients. Finally, to reduce the computational cost, a dimensionality reduction algorithm using Johnson–Lindenstrauss transform is presented. The results developed are illustrated on two real datasets: Yahoo! Tech Buzz dataset and Youstatanalyzer dataset. By using the results developed in the paper, several useful insights can be gleaned from these datasets. First, the changes in ground truth affecting the utility of the agent can be detected by utility maximization behavior in online search. Second, the recovered utility functions satisfy the single crossing property indicating strategic substitute behavior in online search. Finally, due to the large number of videos in YouTube, the utility maximization behavior was verified through the dimensionality reduction algorithm. [ABSTRACT FROM PUBLISHER]

In recent years, with the widespread popularity of the Internet, social media has become an indispensable part of people’s lives. People regard online social media as an essential tool for interaction and communication. Due to the convenience of data acquisition from social media, mental health research on social media has received a lot of attention. The early detection of psychological disorder based on social media can help prevent further deterioration in at-risk people. In this paper, depression detection is performed based on non-verbal (acoustics and visual) behaviors of vlog. We propose a time-aware attention-based multimodal fusion depression detection network (TAMFN) to mine and fuse the multimodal features fully. The TAMFN model is constructed by a temporal convolutional network with the global information (GTCN), an intermodal feature extraction (IFE) module, and a time-aware attention multimodal fusion (TAMF) module. The GTCN model captures more temporal behavior information by combining local and global temporal information. The IFE module extracts the early interaction information between modalities to enrich the feature representation. The TAMF module guides the multimodal feature fusion by mining the temporal importance between different modalities. Our experiments are carried out on D-Vlog dataset, and the comparative experimental results report that our proposed TAMFN outperforms all benchmark models, indicating the effectiveness of the proposed TAMFN model. [ABSTRACT FROM AUTHOR]

The frequent occurrence of extreme weather makes people pay more attention to environmental protection. To cope with the global climate problem, various countries re-plan social development through the concept of low-carbon. As greatly popularized by the Internet, the topic of low carbon concept is spread more through online social media, so it is urgent to understand the user’s attention to low carbon topics in a more intelligent way for subsequent relevant publicity and policy guidance. This paper studies the low-carbon topic of attention in the context of social media. First, the BERT (Bidirectional Encoder Representation from Transformers) model is used to complete the word vector feature extraction of acquired data; Secondly, the FCM method was used to complete the clustering analysis of the main topics in the low-carbon concept, and the PSO method was used to optimize the model. After optimization, the accuracy of clustering for various topics was higher than 80%. For the Esse index of cluster center variance, the method proposed in this article is also close to 10% due to other classic methods; Finally, this paper carried out an application test of low-carbon topics in the region, achieved good results, and made a detailed analysis of the distribution of various topics. It can be predicted that this method will provide more public opinion references for low-carbon development paths in various countries and regions in the future, and provide technical support for information dissemination and analysis under social media.

Although geo-tagged mobility data (e.g., cell phone data and social media data) can be potentially used to estimate individual space–time travel trajectories, they often have low sample rates that only tell travelers’ whereabouts at the sparse sample times while leaving the remaining activities to be estimated with interpolation. This paper proposes a set of time geography-based measures to quantify the accuracy of the trajectory estimation in a robust manner. A series of measures including activity bandwidth and normalized activity bandwidth are proposed to quantify the possible absolute and relative error ranges between the estimated and the ground truth trajectories that cannot be observed. These measures can be used to evaluate the suitability of the estimated individual trajectories from sparsely sampled geo-tagged mobility data for travel mobility analysis. We suggest cutoff values of these measures to separate useful data with low estimation errors and noisy data with high estimation errors. We conduct theoretical analysis to show that these error measures decrease with sample rates and peoples’ activity ranges. We also propose a lookup table-based interpolation method to expedite the computational time. The proposed measures have been applied to 2013 geo-tagged tweet data in New York City, USA, and 2014 cell-phone data in Shenzhen, China. The results illustrate that the proposed measures can provide estimation error ranges for exceptionally large datasets in much shorter times than the benchmark method without using lookup tables. These results also reveal managerial results into the quality of these data for human mobility studies, including their distribution patterns. [ABSTRACT FROM AUTHOR]

This paper introduces a new social media data-analysis tool called FireAnt that allows technical and professional communication researchers, instructors, and students to easily collect, analyze, and visualize social media communication. Problem: Currently, most social media analysis is carried out using custom computer scripts written in programming languages such as Python and R. Although these custom scripts can be very powerful, they create an enormous barrier to those without a strong computational background or the resources to hire a software engineer or data scientist. Key concepts: FireAnt is a freeware, stand-alone, multiplatform social media data-analysis tool designed for both novice and expert computer users. It can be used to collect, analyze, and subsequently visualize social media data as time-series plots, geopositional maps, and network graphs. It can also export results for further processing using traditional corpus tools, statistical packages, and custom scripts. Key lessons: This paper will show how FireAnt can be used to collect social media data related to specific companies, analyze those data, and visualize the data in a variety of ways. Implications for practice: FireAnt has applications in various technical and professional communication settings—for example, as part of a strategy to develop a social media presence and in the analysis of company-customer interactions and user perceptions of goods and services. It can also be used in the technical writing classroom as part of instructor- and student-centered instruction on the safe, appropriate, and effective use of language on social media platforms. [ABSTRACT FROM AUTHOR]

The thirteen papers in this special section focus on social media data analytics with deep intelligence. Big cross-model social media data analytics with deep intelligence aims to handle data sampling from multimodal deep spaces, so as to well characterize the big data. The addressed topic span from the range of human action recognition to affective computing, disaster detection, classification, retrieval, clustering, vehicle reidentification, and data security. [ABSTRACT FROM AUTHOR]

Event popularity prediction over social media is crucial for estimating information propagation scope, decision making, and emergency prevention. However, existing approaches only focus on predicting the occurrences of single attribute such as a message, a hashtag or an image, which are not comprehensive enough for representing complex social event propagation. In this paper, we predict the event popularity, where an event is described as a set of messages containing multiple hashtags. We propose a novel hashtag-influence-based event popularity prediction by mining the impact of an influential hashtag set on the event propagation. Specifically, we first propose a hashtag-influence-based cascade model to select the influential hashtags over an event hashtag graph built by the pairwise hashtag similarity and the topic distribution of event-related hashtags. A novel measurement is proposed to identify the hashtag influence of an event over its content and social impacts. A hashtag correlation-based algorithm is proposed to optimize the seed selection in a greedy manner. Then, we propose an event-fitting boosting model to predict the event popularity by embedding the feature importance over events into the XGBOOST model. Moreover, we propose an event-structure-based method, which incrementally updates the prediction model over social streams. We have conducted extensive experiments to prove the effectiveness and efficiency of the proposed approach. [ABSTRACT FROM AUTHOR]

GIFs are enjoying increasing popularity on social media as a format for data-driven storytelling with visualization; simple visual messages are embedded in short animations that usually last less than 15 seconds and are played in automatic repetition. In this paper, we ask the question, “What makes a data-GIF understandable?” While other storytelling formats such as data videos, infographics, or data comics are relatively well studied, we have little knowledge about the design factors and principles for “data-GIFs”. To close this gap, we provide results from semi-structured interviews and an online study with a total of 118 participants investigating the impact of design decisions on the understandability of data-GIFs. The study and our consequent analysis are informed by a systematic review and structured design space of 108 data-GIFs that we found online. Our results show the impact of design dimensions from our design space such as animation encoding, context preservation, or repetition on viewers understanding of the GIF's core message. The paper concludes with a list of suggestions for creating more effective Data-GIFs. [ABSTRACT FROM AUTHOR]

The popularity of online contents in social media frequently experiences ebb and flow, and thus its evolution often involves different stages, such as burst and valley. Exploring the patterns of popularity evolution, especially how burst forms and decays, and even further, predicting the trends of popularity evolution is both an important research topic and beneficial to support decision making for many applications, such as emergency management, business intelligence, and public security. Previous work on popularity prediction has focused on predicting the popularity volume of online contents, and at most, popularity burst and ignored the exploration of popularity evolution and the prediction of its stages. To fill this gap, in this paper, we propose our method for the popularity stage prediction problem both at the microscopic level and macroscopic level. At the microscopic level, we first extract multiple dynamic factors and infer future evolution stage by considering the contributions of different dynamic factors. At the macroscopic level, we extract the overall evolution patterns of popularity stages and adopt a pattern matching-based method to predict future popularity stages. We evaluate the proposed approach using tweets in SinaWeibo, the most popular Twitter-like social media platform in China. The experimental results show the effectiveness of our proposed approach in predicting popularity evolution stages. [ABSTRACT FROM AUTHOR]

Mobile platforms are prevalently used in the age of social media. They serve a two-sided market to connect users and app developers. In this system, members simultaneously sell products, publish advertisements (ads), and advertise. This paper aims to explore advertising strategies for the mobile platform incorporating their roles as sellers, ad publishers, and advertisers. Specifically, we develop a game-theoretical model which captures the relationship among the works of the platform owner and app developers in a dynamic setting. Our analysis shows that when the platform displays apps’ ads, the owner may be better off to participate in apps’ advertising under certain conditions, rather than always charging them aggressively as suggested by convention wisdom. Surprisingly, although the negative impacts evoked by multiple apps’ entry deserve much attention, it is unnecessary for app developers to take into account when making advertising decisions. We further find that the coordinating bilateral participation in advertising is a new mechanism to improve the profitability. Unlike the result proposed in the previous literature, the mechanism here will cause free-riding among app developers when they participate in the platform’s advertising. Furthermore, when accompanied with the revenue sharing policy, the relatively low participation rates could absolutely eliminate the system inefficiency. [ABSTRACT FROM AUTHOR]

Geo-location data from the check-ins made in online social media offers us information, in new ways, to understand activity-location choices of a large number of people. However, one of the major challenges of using check-in data is that it has missing activities, since users share their activities voluntarily. In this paper, we present a probabilistic modeling approach to reconstruct user activity-location sequences from this incomplete activity participation information. Specifically, we answer the question of how to predict an individual’s next activity, its duration and location given the incomplete trajectory data. The model describes the dynamics of individual activity participation behavior evolving over continuous time. A semi-Markov modeling approach is used to capture the stochastic processes involved in the activity generation mechanism. We present a particle-based Markov chain Monte Carlo sampler to run inference over the model. We further develop an expectation-maximization algorithm to learn the unknown parameters of the model from incomplete trajectory data. Finally, the method is applied to synthetically generated activity-location sequences and a data set of Foursquare check-ins of the users from New York City. Our experiments show that this method can successfully extract the true transition and duration distributions given the incomplete trajectory information. The proposed approach can help building many intelligent transportation applications using check-in data. [ABSTRACT FROM PUBLISHER]

Recent studies have shown that a noticeable percentage of Web search traffic is about social events. While traditional Web sites can only show human-edited events, in this paper, we present a novel system to automatically detect events from search log data and generate storyboards where the events are arranged chronologically. We chose image search log as the resource for event mining, as search logs can directly reflect people’s interests. To discover events from log data, we present a smooth nonnegative matrix factorization framework, which combines the information of query semantics, temporal correlations, search logs, and time continuity. Moreover, we consider the time factor to be an important element, since different events will develop in different time tendencies. In addition, to provide a media-rich and visually appealing storyboard, each event is associated with a set of representative photos arranged along a timeline. These relevant photos are automatically selected from image search results by analyzing image content features. We use celebrities as our test domain, which takes a large percentage of image search traffic. Experiments consisting of Web search traffic on 200 celebrities, for a period of six months, show very encouraging results compared with handcrafted editorial storyboards. [ABSTRACT FROM PUBLISHER]

The rapid development of social media data and the associated growth in volume, velocity, and variety has fostered the idea of using these data to guide traditional remote sensing image retrieval and information extraction tasks. Although important progress has been made in recent years in harvesting spatial and temporal data from social media, the exploitation of these data for decision making still needs further investigation, particularly in the context of its integration with remote sensing and geographic information systems. In this paper, we first discuss the relation between localization techniques and spatial technologies, pointing out their similarities and differences. Then, we provide a discussion on location analysis of social media data, and the fusion of multiple data sources, with specific attention to the integration of social media content (including localization) with remote sensing-based spatial technologies. Next, we provide specific examples addressing the use of social media data to perform information extraction from large remote sensing data repositories. Although significant possibilities for the integration of localization and spatial technologies can be seen in the examples provided, our survey suggests that the convergence of remote sensing and social media data will continue to deeply transform these technologies. [ABSTRACT FROM PUBLISHER]

The hypothesis of this paper is that topics, expressed through large-scale social media networks, approximate electricity utilization events (e.g., using high power consumption devices such as a dryer) with high accuracy. Traditionally, researchers have proposed the use of smart meters to model device-specific electricity utilization patterns. However, these techniques suffer from scalability and cost challenges. To mitigate these challenges, we propose a social media network-driven model that utilizes large-scale textual and geospatial data to approximate electricity utilization patterns, without the need for physical hardware systems (e.g., such as smart meters), hereby providing a readily scalable source of data. The methodology is validated by considering the problem of electricity use disaggregation, where energy consumption rates from a nine-month period in San Diego, coupled with 1.8 million tweets from the same location and time span, are utilized to automatically determine activities that require large or small amounts of electricity to accomplish. The system determines 200 topics on which to detect electricity-related events and finds 38 of these to be valid descriptors of energy utilization. In addition, a comparison with electricity consumption patterns published by domain experts in the energy sector shows that our methodology both reproduces the topics reported by experts, while discovering additional topics. Finally, the generalizability of our model is compared with a weather-based model, provided by the U.S. Department of Energy. [ABSTRACT FROM AUTHOR]

We present a case study on a journey about a personal data collection of carnivorous plant species habitats, and the resulting scientific exploration of location data biases, data errors, location hiding, and data plausibility. While initially driven by personal interest, our work led to the analysis and development of various means for visualizing threats to insight from geo-tagged social media data. In the course of this endeavor we analyzed local and global geographic distributions and their inaccuracies. We also contribute Motion Plausibility Profiles—a new means for visualizing how believable a specific contributor’s location data is or if it was likely manipulated. We then compared our own repurposed social media dataset with data from a dedicated citizen science project. Compared to biases and errors in the literature on traditional citizen science data, with our visualizations we could also identify some new types or show new aspects for known ones. Moreover, we demonstrate several types of errors and biases for repurposed social media data. Please note that people with color impairments may consider our alternative paper version. [ABSTRACT FROM AUTHOR]

In spectrum underlay cognitive radio networks, secondary users (SUs) are allowed to reuse the spectrum allocated to a primary system. The interference between SUs actually carries information and can potentially be exploited to improve the network performance through information-theoretic interference processing. In this paper, we design an optimal joint power and rate control algorithm that maximizes the secondary system utility subject to the interference temperature constraints of primary users based on the capacity-approaching interference processing scheme called as the Han-Kobayashi scheme. The optimal solution is difficult to achieve because the optimization problem is in general non-convex. To make the optimization problem tractable, this paper first transforms the problem into a monotonic optimization problem through exploiting its hidden monotonicity. We then devise an effective algorithm to obtain the global optimal solution to the joint power and rate control problem in the Han-Kobayashi scheme. The key idea behind the proposed algorithm is to construct a sequence of shrinking polyblocks that approximate the upper boundary of the feasible region with increasing precision. Numerical results further show that the achieved utility of our scheme significantly outperforms the utility of conventional schemes which treat the interference between SUs as the noise. [ABSTRACT FROM PUBLISHER]

In this paper, we explore the idea of social role theory (SRT) and propose a novel regularized topic model which incorporates SRT into the generative process of social media content. We assume that a user can play multiple social roles, and each social role serves to fulfil different duties and is associated with a role-driven distribution over latent topics. In particular, we focus on social roles corresponding to the most common social activities on social networks. Our model is instantiated on microblogs, i.e., Twitter and community question-answering (cQA), i.e., Yahoo! Answers, where social roles on Twitter include “originators” and “propagators”, and roles on cQA are “askers” and “answerers”. Both explicit and implicit interactions between users are taken into account and modeled as regularization factors. To evaluate the performance of our proposed method, we have conducted extensive experiments on two Twitter datasets and two cQA datasets. Furthermore, we also consider multi-role modeling for scientific papers where an author’s research expertise area is considered as a social role. A novel application of detecting users’ research interests through topical keyword labeling based on the results of our multi-role model has been presented. The evaluation results have shown the feasibility and effectiveness of our model. [ABSTRACT FROM PUBLISHER]