Hope comes in many forms:out-group expressions of hope override low support and promote reconciliation in conflicts

In conflicts, political attitudes are based to some extent on the perception of the outgroup as sharing the goal of peace and supporting steps to achieve it. However, intractable conflicts are characterized by inconsistent and negative interactions, which prevent clear messages of outgroup support. This problem calls for alternative ways to convey support between groups in conflict. One such method is emotional expressions. The current research tested whether, in the absence of outgroup support for peace, observing expressions of outgroup hope induces conciliatory attitudes. Results from two experimental studies, conducted within the Israeli-Palestinian conflict, revealed support for this hypothesis. Expressions of Palestinian hope induced acceptance of a peace agreement through Israeli hope and positive perceptions of the proposal when outgroup support expressions were low. Findings demonstrate the importance of hope as a means of conveying information within processes of conflict resolution, overriding messages of low outgroup support for peace.

Detecting expressions of blame or praise in text

The growth of social networking platforms has drawn a lot of attentions to the need for social computing. Social computing utilises human insights for computational tasks as well as design of systems that support social behaviours and interactions. One of the key aspects of social computing is the ability to attribute responsibility such as blame or praise to social events. This ability helps an intelligent entity account and understand other intelligent entities’ social behaviours, and enriches both the social functionalities and cognitive aspects of intelligent agents. In this paper, we present an approach with a model for blame and praise detection in text. We build our model based on various theories of blame and include in our model features used by humans determining judgment such as moral agent causality, foreknowledge, intentionality and coercion. An annotated corpus has been created for the task of blame and praise detection from text. The experimental results show that while our model gives similar results compared to supervised classifiers on classifying text as blame, praise or others, it outperforms supervised classifiers on more finer-grained classification of determining the direction of blame and praise, i.e., self-blame, blame-others, self-praise or praise-others, despite not using labelled training data.