Abstract
There is a dual challenge for writing content for the internet: conquering search engines and attracting the attention of target audiences. This chapter proposes a content planning and development approach with a triple focus: main keyword power, target audience, and benefit provided. It argues that keyword power, given by its search volume and effective competition level, provides only an incomplete starting point for creating valuable content, as content effectiveness will ultimately depend on the benefit provided for the target audience. A benefit-driven approach to writing valuable and optimized content is particularly interesting for increasing reach, interaction, and involvement, thus being recommended for inbound and content marketing strategies. The phases of benefit-driven content writing are described, from keyword choice to the main optimization procedures.

Abstract

Abstract
The growth in social media use has given rise to concerns about the impacts it may have on users' psychological well-being. This paper's main objective is to shed light on the effect of social media use on psychological well-being. Building on contributions from various fields in the literature, it provides a more comprehensive study of the phenomenon by considering a set of mediators, including social capital types (i.e., bonding social capital and bridging social capital), social isolation, and smartphone addiction. The paper includes a quantitative study of 940 social media users from Mexico, using structural equation modeling (SEM) to test the proposed hypotheses. The findings point to an overall positive indirect impact of social media usage on psychological well-being, mainly due to the positive effect of bonding and bridging social capital. The empirical model's explanatory power is 45.1%. This paper provides empirical evidence and robust statistical analysis that demonstrates both positive and negative effects coexist, helping to reconcile the inconsistencies found so far in the literature.

Abstract
Although the literature on crisis communication is quite vast, business communication related to global crises (e.g., natural disasters) is largely unexplored. This chapter aims to fill this gap and shed light on brand communication strategies during a pandemic. A netnographic study was carried out with the purpose of identifying brand positioning and communication strategies during the COVID-19 pandemic outbreak and of understanding the engagement of brands' followers during that period. The study included four brands of large Brazilian companies and comprised the analysis of brands' feed on Instagram during the first five weeks of the outbreak in Brazil. Findings enable to identify two distinct profiles: unprepared brands and leading brands. The chapter provides valuable clues for both managers and researchers dealing with crisis communication.

Abstract

Abstract
Massive data broadcast by GPS-equipped vehicles provide unprecedented opportunities. One of the main tasks in order to optimize our transportation networks is to build data-driven real-time decision support systems. However, the dynamic environments where the networks operate disallow the traditional assumptions required to put in practice many off-the-shelf supervised learning algorithms, such as finite training sets or stationary distributions. In this paper, we propose BRIGHT: a drift-aware supervised learning framework to predict demand quantities. BRIGHT aims to provide accurate predictions for short-term horizons through a creative ensemble of time series analysis methods that handles distinct types of concept drift. By selecting neighborhoods dynamically, BRIGHT reduces the likelihood of overfitting. By ensuring diversity among the base learners, BRIGHT ensures a high reduction of variance while keeping bias stable. Experiments were conducted using three large-scale heterogeneous real-world transportation networks in Porto (Portugal), Shanghai (China), and Stockholm (Sweden), as well as with controlled experiments using synthetic data where multiple distinct drifts were artificially induced. The obtained results illustrate the advantages of BRIGHT in relation to state-of-the-art methods for this task.