IADIS INTERNATIONAL JOURNAL ON WWW/INTERNET

19-33

Authors: Ashraf Khalil, Aishah Khan, Tasbeeh Fatima Raza and Salam Abdallah

Emergence of new features on social media platforms have become a daily occurrence. Stories, live videos, memories and various other emerging features are a crucial part of most of the popular social media platforms. Through this study, we conduct a qualitative assessment of user adaptation to these revolutionizing social media features. The aim of this study is to understand user preferences based on platforms, their adaptability and usage of these features. This study derives conclusions based on a semi-structured interview conducted on 18 users in their early twenties in the UAE. The study derives conclusions on changes in user behavior on Facebook, user strategies for managing different audiences and content across social media platforms, views on cross-linking and cross-sharing of social platforms and using social media as a common portal to keep track of daily happenings. The study finds that users treat social media platforms distinctly while tending to share different contents on each platform depending on the nature of the content and the audience of each platform.

50-65

Authors: Isa Inuwa-Dutse, Bello Shehu Bello and Ioannis Korkontzelos

The rise in the number of automated or bot accounts on Twitter engaging in manipulative behaviour is of great concern to studies using social media as a primary data source. Many strategies have been proposed and implemented, however, the sophistication and rate of deployment of bot accounts is increasing rapidly. This impedes and limits the capabilities of detecting bot strategies. Various features broadly related to account profiles, tweet content, network and temporal patterns have been utilised in detection systems. Tweet content has been proven instrumental in this process, but limited to the terms and entities occurring. Given a set of tweets with no obvious pattern, can we distinguish contents produced by social bots from those of humans? What constitutes engagement on Twitter and how can we measure the intensity of engagement? Can we distinguish between bot and human accounts based on engagement intensity? These are important questions whose answer will improve how detection systems operate to combat malicious activities by effectively distinguishing between human and social bot accounts on Twitter. This study attempts to answer these questions by analysing the engagement intensity and lexical richness of tweets produced by human and social bot accounts using large, diverse datasets. Our results show a clear margin between the two classes in terms of engagement intensity and lexical richness. We found that it is extremely rare for a social bot to engage meaningfully with other users and that lexical features significantly improve the performance of classifying both account types. These are important dimensions to explore toward improving the effectiveness of detection systems in combating the menace of social bot accounts on Twitter.

80-94

Authors: Angela Martin, Corentin Canu, Camille Jean and Gabrièle Breda

The main focus of this study is to provide a new framework for business model design by using complex system modelling. The tool offers both entrepreneurs and investors a comprehensive view of value streams by comparing different business models based on the economic shared value creation during the go to market process.