Nature Of Social Media Data Research Articles

Examination of social worlds of risky drinking. Insights from Twitter data analysis.

Rich nature of social media data offers a great opportunity to examine social worlds of its users. Further to wide range of topics being discussed on social media, alcohol-related content is prevalent on social media and studies have found an association between this content and increased consumption of alcohol, cravings for alcohol and addiction. This study analyses social media data to examine social worlds of risky drinking in Victoria, Australia. This study conducted a scoping literature review and two online surveys, one with the general community and the other with health professionals, to determine key words to search for on social media sites. These keywords were used in a social media analytics tool called Talkwalker to generate quantitative and qualitative data on the social media users and their conversations. NVIVO was used for developing categories and themes in a sample of 172 posts. A total of 1,021 results were obtained from Twitter. The main demographic group found to be involved in conversations about drinking alcohol on Twitter was young fathers aged 25-34 years. The culture of alcohol consumption in Victoria for Twitter users is reflective of Australia's drinking culture within which risky drinking, and in particular binge drinking, is normalised.

An application of ontology driven machine learning model challenges for the classification of social media data: a systematic literature review

This systematic literature review aimed to explore the challenges and limitations of applying ontology driven machine learning models to the classification of social media data. Social media platforms generate a vast amount of data that requires automated and reliable classification to facilitate analysis and decision-making. Ontology driven machine learning models offer a promising approach to address this need by harnessing the power of both ontologies and machine learning algorithms to improve accuracy and efficiency. However, the application of such models to social media data classification poses unique challenges due to the complex and dynamic nature of social media data. To address this research gap, a systematic literature search was conducted, and 20 studies were included in the review. The findings of this review suggest that ontology driven machine learning models offer a promising approach to address the challenges of social media data classification. However, the existing literature highlights several challenges that need to be addressed, such as ontology development, feature selection, and model validation. Overall, the review provides insights into the current state of research on ontology driven machine learning models for social media data classification, identifies research gaps, and suggests directions for future investigation.

Analytics of social media data – State of characteristics and application

The spread and use of social networks provide a rich data source that can be used to answer a wide range of research questions from various disciplines. However, the nature of social media data poses a challenge to the analysis. The aim of this study is to provide an in-depth overview of the research that analyzes social media data since 2017. An extensive literature review based on 94 papers led to the findings that clear definitions are neither established nor commonly applied. Predominant research domains include marketing, hospitality and tourism, disaster management, and disruptive technology. The majority of analyzed social media data are taken from Twitter. Sentiment and content analysis are the current prevailing methods. Half of the studies include practical implications. Based on the literature review, clear definitions are provided, and future avenues for high-quality research are suggested.

Value co-creation for open innovation: An evidence-based study of the data driven paradigm of social media using machine learning.

Social media encapsulates one of the most prominent human information behaviours that has rapidly evolved to create a new data-driven paradigm that uses data-intensive digital environments to communicate, collaborate, express opinions and support decisions. This has established social media as a unique information asset for value co-creation as it empowers individuals to actively express opinions and sentiment on all facets of interactions with an external entity. Despite recent research on the theoretical underpinnings of social media in open service innovation, practical demonstrations of actionable insights are limited, mainly due to the voluminous and unstructured nature of social media data. We address this limitation by presenting an evidence-based study that uses machine learning algorithms to generate actionable insights of strategic value from this data-driven paradigm. These outcomes provide fresh perspectives and new thinking that advances social media as an emergent information asset for end-to-end open innovation and incremental value co-creation.

Identification of cyberbullying: A deep learning based multimodal approach

Cyberbullying can be delineated as a purposive and recurrent act, which is aggressive in nature, done via different social media platforms such as Facebook, Twitter, Instagram and others. While existing approaches for detecting cyberbullying concentrate on unimodal approaches, e.g., text or visual based methods, we proposed a deep learning based early identification framework which is a multimodal (textual and visual) approach (inspired by the informal nature of social media data) and performed a broad analysis on vine dataset. Early identification framework predicts a post or a media session as bully or non-bully as early as possible as we have processed information for each of the modalities (both independently and fusion-based) chronologically. Our multimodal feature-fusion based experimental analysis achieved 0.75 F-measure using ResidualBiLSTM-RCNN architecture, which clearly reflects the effectiveness of our proposed framework. All the codes of this study are made publicly available on paper’s companion repository.

Birds of a feather check together: Leveraging homophily for sequential rumour detection

As breaking news unfolds, social media has become the go-to platform to learn about the latest updates from journalists and eyewitnesses on the ground. The fact that anybody can post content in social media during these breaking news leads to posting and diffusion of unverified rumours, which in turn produces uncertainty and increases anxiety. Given the scale of social media contents, automation is key for effective detection of these rumours. In this paper we introduce a novel approach to rumour detection that learns from the sequential dynamics of reporting during breaking news in social media to detect rumours in new stories. Using five Twitter datasets collected during breaking news stories, we experiment with Conditional Random Fields as a sequential classifier that leverages context learnt during an event for rumour detection, which we compare with the state-of-the-art rumour detection system as well as other baselines. In contrast to existing work, our classifier does not need to observe tweets querying a piece of information to deem it a rumour, but instead we detect rumours from the tweet alone by exploiting context learnt during the event. Further, we experiment with homophily as a predictive feature for detecting rumours, i.e. setting forth the hypothesis that a user will be more likely to post a rumour if they follow users who posted or spread rumours in the past. Our classifier achieves state-of-the-art performance, beating competitive baselines as well as outperforming our best baseline with nearly 40% improvement in terms of F1 score. Our research proves the effectiveness of the consideration of the sequential nature of social media data and the usefulness of homophily as a feature for rumour detection.

An empirical case study on Indian consumers' sentiment towards electric vehicles: A big data analytics approach

Today, climate change due to global warming is a significant concern to all of us. India's rate of greenhouse gas emissions is increasing day by day, placing India in the top ten emitters in the world. Air pollution is one of the significant contributors to the greenhouse effect. Transportation contributes about 10% of the air pollution in India. The Indian government is taking steps to reduce air pollution by encouraging the use of electric vehicles. But, success depends on consumer's sentiment, perception and understanding towards Electric Vehicles (EV). This case study tried to capture the feeling, attitude, and emotions of Indian consumers' towards electric vehicles. The main objective of this study was to extract opinions valuable to prospective buyers (to know what is best for them), marketers (for determining what features should be advertised) and manufacturers (for deciding what features should be improved) using Deep Learning techniques (e.g Doc2Vec Algorithm, Recurrent Neural Network (RNN), Convolutional Neural Network (CNN)). Due to the very nature of social media data, big data platform was chosen to analyze the sentiment towards EV. Deep Learning based techniques were preferred over traditional machine learning algorithms (Support Vector Machine, Logistic regression and Decision tree, etc.) due to its superior text mining capabilities. Two years data (2016 to 2018) were collected from different social media platform for this case study. The results showed the efficiency of deep learning algorithms and found CNN yield better results in-compare to others. The proposed optimal model will help consumers, designers and manufacturers in their decision-making capabilities to choose, design and manufacture EV.

What happened then and there: Top-k spatio-temporal keyword query

Social media data, e.g. Tweets, are usually geo-tagged, embedded with creation or posting time, and associated with texts. Nowadays, there is an increasing need for querying such spatio-temporal-text data. In this work, we propose a new type of query, top-k spatio-temporal keyword query (k-STKQ in short), over Twitter-like social media data. A k-STKQ takes a location, a timestamp and a set of keywords as argument, and returns top-k objects that are near the location, close to the timestamp, and relevant to the set of keywords. An example of k-STKQ is to search the tweets mentioning “garage sale” recently sent from some places nearby. The massive amount and dynamic nature of social media data are the primary obstacles towards efficient processing of k-STKQs. In order to return the answers efficiently, we propose a novel index, TiST, for the processing of k-STKQs. TiST partitions the incoming data into subsets, and builds an R-tree index on each subset. The timestamps and texts of the objects are also integrated with the R-trees. To further boost the indexing performance, we propose a routing R-tree based R-tree insertion method, which is inspired by the observation that many sets of objects are similar in their locations. For the texts of objects, we propose a hybrid bitmap-based index, which is space-saving and supports relevance computation. The query processing algorithm is also presented based on the TiST index. We conduct extensive experiments to demonstrate that our solution is capable of providing excellent indexing performance and good query performance.

Multilingual sentiment analysis: from formal to informal and scarce resource languages

The ability to analyse online user-generated content related to sentiments (e.g., thoughts and opinions) on products or policies has become a de-facto skillset for many companies and organisations. Besides the challenge of understanding formal textual content, it is also necessary to take into consideration the informal and mixed linguistic nature of online social media languages, which are often coupled with localised slang as a way to express ‘true’ feelings. Due to the multilingual nature of social media data, analysis based on a single official language may carry the risk of not capturing the overall sentiment of online content. While efforts have been made to understand multilingual sentiment analysis based on a range of informal languages, no significant electronic resource has been built for these localised languages. This paper reviews the various current approaches and tools used for multilingual sentiment analysis, identifies challenges along this line of research, and provides several recommendations including a framework that is particularly applicable for dealing with scarce resource languages.

Home location inference from sparse and noisy data: models and applications

Accurate home location is increasingly important for urban computing. Existing methods either rely on continuous (and expensive) Global Positioning System (GPS) data or suffer from poor accuracy. In particular, the sparse and noisy nature of social media data poses serious challenges in pinpointing where people live at scale. We revisit this research topic and infer home location within 100 m×100 m squares at 70% accuracy for 76% and 71% of active users in New York City and the Bay Area, respectively. To the best of our knowledge, this is the first time home location has been detected at such a fine granularity using sparse and noisy data. Since people spend a large portion of their time at home, our model enables novel applications. As an example, we focus on modeling people’s health at scale by linking their home locations with publicly available statistics, such as education disparity. Results in multiple geographic regions demonstrate both the effectiveness and added value of our home localization method and reveal insights that eluded earlier studies. In addition, we are able to discover the real buzz in the communities where people live.

Adaptive Scaling of Cluster Boundaries for Large-Scale Social Media Data Clustering.

The large scale and complex nature of social media data raises the need to scale clustering techniques to big data and make them capable of automatically identifying data clusters with few empirical settings. In this paper, we present our investigation and three algorithms based on the fuzzy adaptive resonance theory (Fuzzy ART) that have linear computational complexity, use a single parameter, i.e., the vigilance parameter to identify data clusters, and are robust to modest parameter settings. The contribution of this paper lies in two aspects. First, we theoretically demonstrate how complement coding, commonly known as a normalization method, changes the clustering mechanism of Fuzzy ART, and discover the vigilance region (VR) that essentially determines how a cluster in the Fuzzy ART system recognizes similar patterns in the feature space. The VR gives an intrinsic interpretation of the clustering mechanism and limitations of Fuzzy ART. Second, we introduce the idea of allowing different clusters in the Fuzzy ART system to have different vigilance levels in order to meet the diverse nature of the pattern distribution of social media data. To this end, we propose three vigilance adaptation methods, namely, the activation maximization (AM) rule, the confliction minimization (CM) rule, and the hybrid integration (HI) rule. With an initial vigilance value, the resulting clustering algorithms, namely, the AM-ART, CM-ART, and HI-ART, can automatically adapt the vigilance values of all clusters during the learning epochs in order to produce better cluster boundaries. Experiments on four social media data sets show that AM-ART, CM-ART, and HI-ART are more robust than Fuzzy ART to the initial vigilance value, and they usually achieve better or comparable performance and much faster speed than the state-of-the-art clustering algorithms that also do not require a predefined number of clusters.

Interactive Visual Discovering of Movement Patterns from Sparsely Sampled Geo-tagged Social Media Data.

Social media data with geotags can be used to track people's movements in their daily lives. By providing both rich text and movement information, visual analysis on social media data can be both interesting and challenging. In contrast to traditional movement data, the sparseness and irregularity of social media data increase the difficulty of extracting movement patterns. To facilitate the understanding of people's movements, we present an interactive visual analytics system to support the exploration of sparsely sampled trajectory data from social media. We propose a heuristic model to reduce the uncertainty caused by the nature of social media data. In the proposed system, users can filter and select reliable data from each derived movement category, based on the guidance of uncertainty model and interactive selection tools. By iteratively analyzing filtered movements, users can explore the semantics of movements, including the transportation methods, frequent visiting sequences and keyword descriptions. We provide two cases to demonstrate how our system can help users to explore the movement patterns.

Dynamic non-parametric joint sentiment topic mixture model

The reviews in social media are produced continuously by a large and uncontrolled number of users. To capture the mixture of sentiment and topics simultaneously in reviews is still a challenging task. In this paper, we present a novel probabilistic model framework based on the non-parametric hierarchical Dirichlet process (HDP) topic model, called non-parametric joint sentiment topic mixture model (NJST), which adds a sentiment level to the HDP topic model and detects sentiment and topics simultaneously from reviews. Then considered the dynamic nature of social media data, we propose dynamic NJST (dNJST) which adds time decay dependencies of historical epochs to the current epochs. Compared with the existing sentiment topic mixture models which are based on latent Dirichlet allocation (LDA), the biggest difference of NJST and dNJST is that they can determine topic number automatically. We implement NJST and dNJST with online variational inference algorithms, and incorporate the sentiment priors of words into NJST and dNJST with HowNet lexicon. The experiment results in some Chinese social media dataset show that dNJST can effectively detect and track dynamic sentiment and topics.

Uncovering information from social media hyperlinks: An investigation of twitter

Analyzing hyperlink patterns has been a major research topic since the early days of the web. Numerous studies reported uncovering rich information and methodological advances. However, very few studies thus far examined hyperlinks in the rapidly developing sphere of social media. This paper reports a study that helps fill this gap. The study analyzed links originating from tweets to the websites of 3 types of organizations (government, education, and business). Data were collected over an 8‐month period to observe the fluctuation and reliability of the individual data set. Hyperlink data from the general web (not social media sites) were also collected and compared with social media data. The study found that the 2 types of hyperlink data correlated significantly and that analyzing the 2 together can help organizations see their relative strength or weakness in the two platforms. The study also found that both types of inlink data correlated with offline measures of organizations' performance. Twitter data from a relatively short period were fairly reliable in estimating performance measures. The timelier nature of social media data as well as the date/time stamps on tweets make this type of data potentially more valuable than that from the general web.

Communities of Communication: Making Sense of the “Social” in Social Media

As social media usage permeates people's lives, an increasing portion of their daily behavior leaves digital traces to be used by researchers. Social scientists can hope to gain new insight into the previously hidden but digitally recorded aspects of our digital social lives. Beyond aggregate and individual-level studies of user behavior, the digital traces also enable scientific examination of the structure of social interaction through networks. At the same time, the large scale and networked nature of social media data pose a new set of challenges to be overcome through the development of sound methodologies. We take stock of current methodological promises and challenges in social media analysis. Community detection, a set of methods for the discovery of closely knit groups, is then presented as an intermediary step that enables application of existing traditional and network analytical approaches in a smaller setting more suited to social scientific questions. In closing, we argue that this network proximity-based clustering is often more useful for social media analysis than demographic grouping.