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Social Media is one of the largest platforms to voluntarily communicate thoughts. With increase in multimedia data on social networking websites, information about human behaviour is increasing. This user-generated data are present on the internet in different modalities including text, images, audio, video, gesture, etc. The purpose of this paper is to consider multiple variables for event detection and analysis including weather data, temporal data, geo-location data, traffic data, weekday’s data, etc.

In this paper, evolution of different approaches have been studied and explored for multivariate event analysis of uncertain social media data.

Based on burst of outbreak information from social media including natural disasters, contagious disease spread, etc. can be controlled. This can be path breaking input for instant emergency management resources. This has received much attention from academic researchers and practitioners to study the latent patterns for event detection from social media signals.

This paper provides useful insights into existing methodologies and recommendations for future attempts in this area of research. An overview of architecture of event analysis and statistical approaches are used to determine the events in social media which need attention.

Delivering messages and information to potentially interested users is one of the distinguishing applications of online enterprise social network (ESN). The purpose of this paper is to provide insights to better understand the repost preferences of users and provide personalized information service in enterprise social media marketing.

It is accomplished by constructing a target audience identification framework. Repost preference latent Dirichlet allocation (RPLDA) topic model topic model is proposed to understand the mass user online repost preferences toward different contents. A topic-oriented preference metric is proposed to measure the preference degree of individual users. And the function of reposting forecasting is formulated to identify target audience.

The empirical research shows the following: a total of 20 percent of the repost users in ESN represent the key active users who are particularly interested in the latent topic of messages in ESN and fits Pareto distribution; and the target audience identification framework can successfully identify different target key users for messages with different latent topics.

The findings should motivate marketing managers to improve enterprise brand by identifying key target audience in ESN and marketing in a way that truthfully reflects personalized preferences.

This study runs counter to most current business practices, which tend to use simple popularity to seek important users. Adaptively and dynamically identifying target audience appears to have considerable potential, especially in the rapidly growing area of enterprise social media information service.

The purpose of this paper is to investigate how government affairs micro-blog (also referred to as GAM) are applied to the disclosure of government emergency information in China, to identify its existing problems and to provide solutions.

In this paper, online research, case analysis and other methods were used to analyze the application status of China’s Government micro-blog in emergency information disclosure in recent years. Based on the relevant data and cases, a systematic theoretical research is conducted according to the established research framework.

There are some problems in the application of GAM to crisis management, such as insufficient information dissemination, incomplete information disclosure, fragmentation of information and lack of dynamic updating and communication. So, it is necessary to strengthen the organization and management of GAM, establish a perfect emergency management mechanism of GAM, increase the positive influence of GAM on public opinions and establish an evaluation accountability system of administrative micro-blog management.

The analysis of the application of GAM to the disclosure of government emergency information and the proposed strategies for improving its performance are all original, and they are both meaningful to more effective usage of GAM and facilitation of government emergency information disclosure.

The aim of this paper is to show that online learning behaviors are dictated by both personal characteristics and regional differences.

Data were collected from 16,133 users in 25 regions of Taiwan. The paper examined usage behaviors by looking at 11 items of categorical variables about online learning. This study implemented a multi‐level latent class model to investigate online learning behavior patterns that exhibit regional differences.

The results showed that online learning patterns do exhibit regional differences, as the regional segments are dictated by the individual segments of different use patterns. For instance, the urban area segment comprised a higher proportion of members who are good at using the internet. The rural area segment made up a higher proportion of members who occasionally use the internet. Interestingly, rural users went online more often than urban users when in search of e‐learning or entertainment. On the other hand, the individual segments are dictated by users' personal characteristics. For instance, younger people are good at employing online learning and entertainment services. Moreover, those who use many types of online applications pay less respect to intellectual property rights than those who only use a few types of applications.

By using a massive amount of survey data to show regional differences in online learning behavior patterns, the findings herein will help internet service providers form an applicable guideline for developing service strategies of higher service satisfaction between products and users' needs.

Hate speech in recent times has become a troubling development. It has different meanings to different people in different cultures. The anonymity and ubiquity of the social media provides a breeding ground for hate speech and makes combating it seems like a lost battle. However, what may constitute a hate speech in a cultural or religious neutral society may not be perceived as such in a polarized multi-cultural and multi-religious society like Nigeria. Defining hate speech, therefore, may be contextual. Hate speech in Nigeria may be perceived along ethnic, religious and political boundaries. The purpose of this paper is to check for the presence of hate speech in social media platforms like Twitter, and to what degree is hate speech permissible, if available? It also intends to find out what monitoring mechanisms the social media platforms like Facebook and Twitter have put in place to combat hate speech. Lexalytics is a term coined by the authors from the words lexical analytics for the purpose of opinion mining unstructured texts like tweets.

This research developed a Python software called polarized opinions sentiment analyzer (POSA), adopting an ego social network analytics technique in which an individual’s behavior is mined and described. POSA uses a customized Python N-Gram dictionary of local context-based terms that may be considered as hate terms. It then applied the Twitter API to stream tweets from popular and trending Nigerian Twitter handles in politics, ethnicity, religion, social activism, racism, etc., and filtered the tweets against the custom dictionary using unsupervised classification of the texts as either positive or negative sentiments. The outcome is visualized using tables, pie charts and word clouds. A similar implementation was also carried out using R-Studio codes and both results are compared and a t-test was applied to determine if there was a significant difference in the results. The research methodology can be classified as both qualitative and quantitative. Qualitative in terms of data classification, and quantitative in terms of being able to identify the results as either negative or positive from the computation of text to vector.

The findings from two sets of experiments on POSA and R are as follows: in the first experiment, the POSA software found that the Twitter handles analyzed contained between 33 and 55 percent hate contents, while the R results show hate contents ranging from 38 to 62 percent. Performing a t-test on both positive and negative scores for both POSA and R-studio, results reveal p-values of 0.389 and 0.289, respectively, on an α value of 0.05, implying that there is no significant difference in the results from POSA and R. During the second experiment performed on 11 local handles with 1,207 tweets, the authors deduce as follows: that the percentage of hate contents classified by POSA is 40 percent, while the percentage of hate contents classified by R is 51 percent. That the accuracy of hate speech classification predicted by POSA is 87 percent, while free speech is 86 percent. And the accuracy of hate speech classification predicted by R is 65 percent, while free speech is 74 percent. This study reveals that neither Twitter nor Facebook has an automated monitoring system for hate speech, and no benchmark is set to decide the level of hate contents allowed in a text. The monitoring is rather done by humans whose assessment is usually subjective and sometimes inconsistent.

This study establishes the fact that hate speech is on the increase on social media. It also shows that hate mongers can actually be pinned down, with the contents of their messages. The POSA system can be used as a plug-in by Twitter to detect and stop hate speech on its platform. The study was limited to public Twitter handles only. N-grams are effective features for word-sense disambiguation, but when using N-grams, the feature vector could take on enormous proportions and in turn increasing sparsity of the feature vectors.

The findings of this study show that if urgent measures are not taken to combat hate speech there could be dare consequences, especially in highly polarized societies that are always heated up along religious and ethnic sentiments. On daily basis tempers are flaring in the social media over comments made by participants. This study has also demonstrated that it is possible to implement a technology that can track and terminate hate speech in a micro-blog like Twitter. This can also be extended to other social media platforms.

This study will help to promote a more positive society, ensuring the social media is positively utilized to the benefit of mankind.

The findings can be used by social media companies to monitor user behaviors, and pin hate crimes to specific persons. Governments and law enforcement bodies can also use the POSA application to track down hate peddlers.

Given a product or service, the number of its installed user base has a significant positive effect on the existing users’ loyalty and new users’ conversion. This effect is conceptualized as network externalities in economics. Network externalities are supposed to be particularly striking in nowadays online business-to-business (B2B) platforms, but yet the mystery behind their effects on user loyalty to online B2B platforms remains to be delicately unraveled. The purpose of this paper is to discover the factors driving users’ loyalty, especially buyers’ loyalty, to online B2B platforms, by highlighting the impacts of network externalities on loyalty and other mediating factors.

A conceptual model of buyer loyalty under network externalities is elaborated. The reliability and validity of the instruments of the latent model constructs are assessed by confirmatory factor analysis, and the hypothesized causal relationships among the constructs are tested by structural equation modeling, on 710 valid buyer samples collected from a famous online B2B platform in China.

The analysis demonstrates that: perceived value, user satisfaction and switching costs are the major predictors of buyer loyalty to online B2B platforms characterized by network externalities; network externalities positively account for buyer loyalty by contributing to perceived value, user satisfaction and switching costs; and direct network externality (measured by perceived network size and perceived external prestige) has a significant effect on indirect network externality (measured by perceived compatibility and perceived complementarity).

The findings allow the authors to conclude meaningful managerial implications for online B2B service providers to build up loyal user bases through improving users’ perceptions of network externalities, switching costs and value.

This study aims to understand pumpkin waste awareness among people by converting unstructured quantitative data into insightful information to understand the public's awareness of pumpkin waste during Halloween.

To fulfil the study's purpose, we extracted Halloween-related tweets by employing #halloween and #pumpkin hashtags and then investigated Halloween-related tweets via a topic modelling approach, specifically Latent Dirichlet Allocation. The tweets were collected from the UK between October 25th and November 7th, 2020. The analysis was completed with 11,744 tweets.

The topic modelling results revealed that people are aware of the pumpkin waste during Halloween. Furthermore, people tweet to reduce pumpkin waste by sharing recipes for using leftover pumpkins.

The study offers a novel approach to convert social media data into meaningful knowledge about public perception of food waste. This paper contributes to food waste literature by revealing people's awareness of pumpkin waste during Halloween using social media analytics. Norm activation model and communicative ecology theory are used for the theoretical underpinning of topic modelling.

This paper aims to examine social media communication that may consist of a database for online research and may create an online imagined community that follows special language symbols and shares common beliefs in a similar way to Anderson’s imagined communities.

Well-known databases were searched in the available literature for specific keywords which were associated with the imagined community, and methodological tools such as online interviews, content analysis, archival analysis and social media.

The paper discusses the use of multiple measures, such as document and archival analysis, online interviews and content analysis, which may derive from the online imagined community that social media create. Social media may in fact provide useful data that are available for research, yet are relatively understudied and not fully used in communication research, not to mention in archival services. Comparison takes place between online community’s characteristics and traditional communication research. Information and communication technologies (ICTs) and social media’s use of special language requirements may categorise discussion of these potential data, based on specific symbols, topical threads, purposeful samples and catering for longitudinal studies.

Social media have not been fully implemented for online communication research yet. Online communication may offer significant implications for marketers, advertisers of a company or for an organisation to do research on or for their target groups. The role of libraries and information professionals can be significant in data gathering and the dissemination of such information using ICTs and renegotiating their role.

The theoretical contribution of this paper is the examination of the creation of belonging in an online community, which may offer data that can be further examined and has all the credentials to do so, towards the enhancement of online communication research. The applications of social media to research and the use by and for information professionals and marketers may in fact contribute to the management of an online community with people sharing similar ideas. The connection of the online imagined community with social media for research has not been studied, and it would further enhance understanding from organisations or marketers.

Do public opinion and political sentiments expressed on Twitter during election campaign have a meaning and message? Are they inferential, that is, can they be used to estimate the political mood prevailing among the masses? Can they also be used to reliably predict the election outcome? To answer these in the Indian context, the 2014 general election was chosen.

Tweets posted on the leading parties during the voting and crucial campaign periods were mined and manual sentiment analysis was performed on them.

A strong and positive correlation was observed between the political sentiments expressed on Twitter and election results. Further, the Time Periods during which the tweets were mined were found to have a moderating effect on this relationship.

This study showed that the month preceding the voting period was the best to predict the vote share with Twitter data – with 83.9% accuracy.

Twitter has become an important public communication tool in India, and as the study results reinstate, it is an ideal research tool to gauge public opinion.

The reviews of online tourism have not been taken advantage of effectively because the text data of such reviews is enormous and its current, in-depth research is still in infancy. Therefore, it is expected that the text data could be processed by the method of text mining to better understand the implicit information. The purpose of this paper is to contribute to tourism practitioners and tourists to conveniently use the texts through appropriate visualization processing techniques. In particular, time-changing reviews can be used to reflect the changes in tourists’ feedback and concerns.

Latent semantic analysis is a new branch of semantics. Every term in the document can be regarded as a single point in multi-dimensional space. When a document with semantics comes into such space, the distribution of the document is not random, but will obey some type of semantic structure.

First, overall grasping for the big data is applicable. Second, propose a direct method is proposed that allows more non-language processing researchers or proprietors to use the data. Lastly, the results of changes in different spans of times are investigated.

This paper proposes an approach to disclose a significant number of travel comments from different years that may generate new ideas for tourism. The authors put forward a processing approach to deal with large amounts of texts of comments. Using the case study of Mt. Lushan, the various changes of travel reviews over the years are successfully visualized and displayed.