Real-Time Tweet Analytics Using Hybrid Hashtags on Twitter Big Data Streams

Zhou

et al. 2021

Scientific Programming

Social Internet of Things (SIoT) is a variation of social networks that adopt the property of peer-to-peer networks, in which connections between the things and social actors are automatically established. SIoT is a part of various organizations that inherit the social interaction, and these organizations include industries, institutions, and other establishments. Triadic closure and homophily are the most commonly used measures to investigate social networks’ formation and nature, where both measures are used exclusively or with statistical models. The triadic closure patterns are mapped for actors’ communication behavior over a location-based social network, affecting the homophily. In this study, we investigate triads emergence in homophilic social networks. This evaluation is based on the empirical review of triads within social networks (SNs) formed on Big Data. We utilized a large location-based dataset for an in-depth analysis, the Chinese telecommunication-based anonymized call detail records (CDRs). Two other openly available datasets, Brightkite and Gowalla, were also studied. We identified and proposed three social triad classes in a homophilic network to feature the correlation between social triads and homophily. The study opened a promising research direction that relates the variation of homophily based on closure triads nature. The homophilic triads are further categorized into transitive and intransitive groups. As our concluding research objective, we examined the relative triadic throughput within a location-based social network for the given datasets. The research study attains significant results highlighting the positive connection between homophily and a specific social triad class.

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Correlation between Triadic Closure and Homophily Formed over Location-Based Social Networks

Zhou

et al. 2021

Scientific Programming

“…Annotating data is labor-intensive and several solutions have been proposed to reduce the coding work to a minimum; such as employing labeled data from a related task but different corpus, or using hashtags or well-defined keywords as annotations instead of human codings (Hasan et al, 2014;Gupta & Hewett, 2020). Next to that, semi-supervised learning (Van Engelen & Hoos, 2020) and transfer learning (Terechshenko et al, 2020) can be relevant to train a classifier when labeled data is scarce.…”

Section: Introductionmentioning

confidence: 99%

Comparing Automated Content Analysis Methods To Distinguish Issue Communication by Political Parties on Twitter

et al. 2021

Party competition in Western Europe is increasingly focused on "issue competition", which is the selective emphasis on issues by parties. The aim of this paper is to contribute methodologically to the increasing number of studies that deal with different aspects of parties' issue competition and communication. We systematically compare the value and shortcomings of three exploratory text representation approaches to study the issue communication of parties on Twitter. More specifically, we analyze which issues separate the online communication of one party from that of the other parties and how consistent party communication is. Our analysis was performed on two years of Twitter data from six Belgian political parties, comprising of over 56,000 political tweets. The results indicate that our exploratory approach is useful to study how political parties profile themselves on Twitter and which strategies are at play. Second, our method allows to analyze communication of individual politicians which contributes to classical literature on party unity and party discipline. A comparison of our three methods shows a clear trade-off between interpretability and discriminative power, where a combination of all three simultaneously provides the best insights.Electronic copy available at: https://ssrn.com/abstract=3782027 them an electoral advantage. Furthermore, the fragmentation of party landscapes across Europe in recent decades has increased the number of issues parties put forward. This explains why party competition in Western Europe has increasingly focused on the battle about which issues should dominate the party political agenda, i.e. "issue competition" (Green-Pedersen, 2007). The growing importance of issues in party politics, is also reflected by the rising attention for and proliferation of theories dealing with issue competition and communication (e.g. De Sio & Lachat, 2020).Traditionally, research would examine party manifestos, campaign ads or press releases to study strategic issue communication choices (Tresch et al., 2017). However, nowadays social media represents an interesting alternative, as it is perhaps the most widely accessible form of party communication, with higher temporal adaptability and interaction potential (De Sio & Lachat, 2020). There is growing scholarly interest in parties' issue communication and strategies on social media (Vargo et al., 2014;Van Dalen et al., 2015;Van Ditmars et al., 2020). However, the high volatility of social media communication in combinations with relatively short and less formal text complicates automatic coding methods and party-level analysis. Therefore, the main aim of this study is to contribute to the rapid increase of studies that deal with different aspects of parties' issue communication on social media.Especially Twitter is increasingly used by political parties and politicians to communicate with citizens, but even more so with opinion leaders and journalists (Jungherr, 2016;Vargo et al., 2014). We accept the press-release assumption of political pa...

“…Twitter is one of the most accessed social media, where mass people express their lifestyle and thoughts. The daily count of tweets reaches the 500 million mark regularly, which involves texts, images, and videos [6]. This high volume and variety of data makes tweets as a candidate for unstructured big data source.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Deep Learning Model to Predict the Impact of COVID-19 on Mental Health form Social Media Big Data

Ghosh¹,

Mha²,

Mja³

et al. 2021

Preprint

The novel coronavirus disease (COVID-19) pandemic is provoking a prevalent consequence on mental health because of less interaction among people, economic collapse, negativity, fear of losing jobs, and death of the near and dear ones. To express their mental state, people often are using social media as one of the preferred means. Due to reduced outdoor activities, people are spending more time on social media than usual and expressing their emotion of anxiety, fear, and depression. On a daily basis, about 2.5 quintillion bytes of data are generated on social media, analyzing this big data can become an excellent means to evaluate the effect of COVID-19 on mental health. In this work, we have analyzed data from Twitter microblog (tweets) to find out the effect of COVID-19 on peoples mental health with a special focus on depression. We propose a novel pipeline, based on recurrent neural network (in the form of long-short term memory or LSTM) and convolutional neural network, capable of identifying depressive tweets with an accuracy of 99.42%. Preprocessed using various natural language processing techniques, the aim was to find out depressive emotion from these tweets. Analyzing over 571 thousand tweets posted between October 2019 and May 2020 by 482 users, a significant rise in depressing tweets was observed between February and May of 2020, which indicates as an impact of the long ongoing COVID-19 pandemic situation.