Irena Pletikosa Cvijikj

Online engagement factors on Facebook brand pages

Social networks have become an additional marketing channel that could be integrated with the traditional ones as a part of the marketing mix. The change in the dynamics of the marketing interchange between companies and consumers as introduced by social networks has placed a focus on the non-transactional customer behavior. In this new marketing era, the terms engagement and participation became the central non-transactional constructs, used to describe the nature of participants’ specific interactions and/or interactive experiences. These changes imposed challenges to the traditional one-way marketing, resulting in companies experimenting with many different approaches, thus shaping a successful social media approach based on the trial-and-error experiences. To provide insights to practitioners willing to utilize social networks for marketing purposes, our study analyzes the influencing factors in terms of characteristics of the content communicated by the company, such as media type, content type, posting day and time, over the level of online customer engagement measured by number of likes, comments and shares, and interaction duration for the domain of a Facebook brand page. Our results show that there is a different effect of the analyzed factors over individual engagement measures. We discuss the implications of our findings for social media marketing.

Friendship Selection in the Social Internet of Things: Challenges and Possible Strategies

The Internet of Things (IoT) is expected to be overpopulated by a very large number of objects, with intensive interactions, heterogeneous communications, and millions of services. Consequently, scalability issues will arise from the search of the right object that can provide the desired service. A new paradigm known as Social Internet of Things (SIoT) has been introduced and proposes the integration of social networking concepts into the Internet of Things. The underneath idea is that every object can look for the desired service using its friendships, in a distributed manner, with only local information. In the SIoT it is very important to set appropriate rules in the objects to select the right friends as these impact the performance of services developed on top of this social network. In this work, we addressed this issue by analyzing possible strategies for the benefit of overall network navigability. We first propose five heuristics, which are based on local network properties and that are expected to have an impact on the overall network structure. We then perform extensive experiments, which are intended to analyze the performance in terms of giant components, average degree of connections, local clustering, and average path length. Unexpectedly, we discovered that minimizing the local clustering in the network allowed for achieving the best results in terms of average path length. We have conducted further analysis to understand the potential causes, which have been found to be linked to the number of hubs in the network.

A case study of the effects of moderator posts within a facebook brand page

Social networks have become an additional marketing channel that could be integrated with the traditional ones, such as news and television media, as well as online channels. User participation as a main feature of the social networks imposes challenges to the traditional one-way marketing, resulting in companies experimenting with many different approaches, thus shaping a successful social media approach based on the trial-and-error experiences. Our study analyses the effects of moderator posts characteristics such as post type, category and posting day, on the user interaction in terms of number of comments and likes, and interaction duration for the domain of a sponsored Facebook brand page. Our results show that there is a significant effect of the post type and category on likes and comments (p < 0.0001) as well as on interaction duration (p < 0.01). The posting day has effect only over the comments ratio (p < 0.05). We discuss the implications of our findings for social media marketing.

Understanding social media marketing: a case study on topics, categories and sentiment on a Facebook brand page

Social networks have changed the way information is delivered to the customers, shifting from traditional one-to-many to one-to-one communication. Opinion mining and sentiment analysis offer the possibility to understand the user-generated comments and explain how a certain product or a brand is perceived. Classification of different types of content is the first step towards understanding the conversation on the social media platforms. Our study analyses the content shared on Facebook in terms of topics, categories and shared sentiment for the domain of a sponsored Facebook brand page. Our results indicate that Product, Sales and Brand are the three most discussed topics, while Requests and Suggestions, Expressing Affect and Sharing are the most common intentions for participation. We discuss the implications of our findings for social media marketing and opinion mining.

The Toolkit Approach for End-user Participation in the Internet of Things

Today, there are many end-user programming tools available, but in the Internet of Things domain, this concept is relatively new. Some pioneer examples include solutions, such as d.tools and Pachube, but also Web2.0, Mash-ups, Twitter and Facebook are suitable backplanes for this kind of applications. Another level of development support is various hardware concepts and solutions, such as RFIDs, Arduino, Violet, NFC, barcodes and many more. Appropriate user programmability could transform a system, multiplying the effectiveness of programmers and users. This article discusses how end-users can be empowered with new building blocks and tools, analogous to those that were emerging during the early phases of Internet growth. Accelerators, frameworks and toolkits are introduced, which would allow everybody to participate in the Internet of Things in the same manner as in the Internet through Wikis, Blogs etc.

Network navigability in the social Internet of Things

The Internet of Things is expected to be overpopulated by a very large number of objects, with intensive interactions, heterogeneous communications and millions of services. Consequently, scalability issues will arise from the search of the right object that can provide the desired service. A new paradigm known as Social Internet of Things has been introduced and proposes the integration of social networking concepts into the Internet of Things. The underneath idea is that every object can look for the desired service using its friendships, in a distributed manner. However, in the resulting network, every object will still have to manage a large number of friends, slowing down the search of the services. In this work, we intend to address this issue by analyzing possible strategies to drive the objects to select the appropriate links for the benefit of overall network navigability.

The Effect of Post Type, Category and Posting Day on User Interaction Level on Facebook

Social networks are becoming an additional marketing channel that could be integrated with the traditional ones as a part of the marketing mix. However, traditional advertising techniques are not applicable for the social media platforms, resulting in companies experimenting with many different approaches, thus shaping a successful social media strategies based on their own experiences. To gain a more general understanding, our study analyzes the effects of moderator post characteristics, such as post type, category and posting day, on the user interaction in terms of number of comments and likes, as well as interaction duration. We present the results obtained from 14 Face book brand pages. Our results show that there is a significant effect of the post type and category on number of likes and comments as well as on interaction duration. Based on these results, we could show clear evidence of moderator posts increasing fan activity. We discuss the implications of our findings for social media marketing.

Evaluation framework for social media brand presence

Social media have transformed the traditional marketing communication, resulting in companies evolving their customer approach and integrating social media into their marketing strategies. Although numerous examples of using social media platforms for marketing purposes exist, and despite the various efforts from the companies and the general popularity of the medium, measuring the effectiveness is elusive. An approach towards overcoming these challenges is examination of the activities undertaken by the companies and the consumers’ responses to them in the form of measurements and use of analysis tools. To contribute in this direction, we propose an evaluation framework that allows companies to perform social media analytics through continuous monitoring of the content and activities on their social media marketing channels, and to measure the effectiveness of social media utilization for marketing purposes. We describe the specific methods and illustrate the application of the proposed approach using a Facebook brand page case study. Finally, we discuss the benefits it brings to the companies.

Understanding the user generated content and interactions on a Facebook brand page

Social networks provide the technological platform for individuals to connect, produce and share content online. As such, they are becoming an additional marketing channel that could be integrated with the traditional ones as a part of the marketing mix. In order to contribute to the companies’ understanding of utilisation of social networks as a marketing platform, our study approaches the problem from the perspective of understanding the user generated content and the interactions. First we analyse the topics, intentions for participation and emotions shared by the users on a Facebook brand page. Second, we analyse the activities and interactions, in terms of their evolution over time and dependency on the community size. Finally, we discuss the implications of the obtained results for social media marketing.

CityWatch: the personalized crime prevention assistant

Motivated by rising levels of crime against property and findings in criminology research, we are developing CityWatch - the first mobile application that supports crime prevention behavior at community level. CityWatch leverages data on past crime incidents, which are sourced both from trustworthy sources, like the national census and the insurance industry, and from its users through crowd-sourcing. It applies machine learning algorithms to analyze the past incidents together with further data characterizing the living areas and learns common patterns of crime. These patterns are then leveraged in a general forecasting component, as well as in generating personalized risk profiles and crime prevention tips for registered users based on their account information. The results are visualized in an interactive map, where users can analyze past crime in their neighborhood and view predictions of future crime. Users can report a new crime and opt to receive notifications about new incidents in their proximity or area of residence.