Leader attributes, behavior, and leadership outcomes: An enrichment of implicit leadership theories

This thesis enriches the understanding of implicit leadership and followership theories (ILTs and IFTs) by revealing the benefits of persons holding the leader role as being perceived as matching with certain attributes that are more commonly associated with followership prototypes. Specifically, quantitative results from Studies 1-3 of show that attributes previously associated with ILTs and IFTs can usefully be categorized into three groups: (1) leader-specific prototypical attributes (LSP; i.e., attributes that are commonly specifically used to describe a typical leader in the organization), (2) follower-specific prototypical attributes (FSP; i.e., attributes that are commonly specifically used to describe a typical follower), and (3) role-common prototypical attributes (CP; i.e., attributes that are viewed to be possessed by both leaders and followers). Using these empirically derived categories of attributes, I examined the unique contributions of leaders’ FSP to leadership outcomes in two additional studies. Results from a follower-only, cross-sectional dataset (Study 4) demonstrated the unique value of FSP in predicting followers’ perceptions of their leader’s consideration behavior, which was further related to those followers’ affective commitment towards the leader and organizational citizenship behavior. These findings were further supported by a multi-wave, leader-follower matched design (Study 5). Moreover, the relationship between leader self-views on the three sets of attributes and that from followers’ eyes were investigated, as well as the direct effects of leader self-views on FSP and their structural and considerate behaviors (Study 5). Overall, this thesis has important implications for the current literature

Two Rules on the Protein-Ligand Interaction

So far, we still lack a clear molecular mechanism to explain the protein-ligand interaction on the basis of electronic structure of a protein. By combining the calculation of the full electronic structure of a protein along with its hydrophobic pocket and the perturbation theory, we found out two rules on the protein-ligand interaction. One rule is the interaction only occurs between the lowest unoccupied molecular orbitals (LUMOs) of a protein and the highest occupied molecular orbital (HOMO) of its ligand, not between the HOMOs of a protein and the LUMO of its ligand. The other rule is only those residues or atoms located both on the LUMOs of a protein and in a surface pocket of a protein are activity residues or activity atoms of the protein and the corresponding pocket is the ligand binding site. These two rules are derived from the characteristics of energy levels of a protein and might be an important criterion of drug design

LextPT: A reliable and efficient vocabulary size test for L2 Portuguese proficiency

Vocabulary size has been repeatedly shown to be a good indicator of second language (L2) proficiency. Among the many existing vocabulary tests, the LexTALE test and its equivalents are growing in popularity since they provide a rapid (within 5 minutes) and objective way to assess the L2 proficiency of several languages (English, French, Spanish, Chinese, and Italian) in experimental research. In this study, expanding on the standard procedure of test construction in previous LexTALE tests, we develop a vocabulary size test for L2 Portuguese proficiency: LextPT. The selected lexical items fall in the same frequency interval in European and Brazilian Portuguese, so that LextPT accommodates both varieties. A large-scale validation study with 452 L2 learners of Portuguese shows that LextPT is not only a sound and effective instrument to measure L2 lexical knowledge and indicate the proficiency of both European and Brazilian Portuguese, but is also appropriate for learners with different L1 backgrounds (e.g. Chinese, Germanic, Romance, Slavic). The construction of LextPT, apart from joining the effort to provide a standardised assessment of L2 proficiency across languages, shows that the LexTALE tests can be extended to cover different varieties of a language, and that they are applicable to bilinguals with different linguistic experience

Sentiment Paradoxes in Social Networks: Why Your Friends Are More Positive Than You?

Most people consider their friends to be more positive than themselves, exhibiting a Sentiment Paradox. Psychology research attributes this paradox to human cognition bias. With the goal to understand this phenomenon, we study sentiment paradoxes in social networks. Our work shows that social connections (friends, followees, or followers) of users are indeed (not just illusively) more positive than the users themselves. This is mostly due to positive users having more friends. We identify five sentiment paradoxes at different network levels ranging from triads to large-scale communities. Empirical and theoretical evidence are provided to validate the existence of such sentiment paradoxes. By investigating the relationships between the sentiment paradox and other well-developed network paradoxes, i.e., friendship paradox and activity paradox, we find that user sentiments are positively correlated to their number of friends but rarely to their social activity. Finally, we demonstrate how sentiment paradoxes can be used to predict user sentiments.Comment: The 14th International AAAI Conference on Web and Social Media (ICWSM 2020

From Fake News to #FakeNews: Mining Direct and Indirect Relationships among Hashtags for Fake News Detection

The COVID-19 pandemic has gained worldwide attention and allowed fake news, such as ``COVID-19 is the flu,'' to spread quickly and widely on social media. Combating this coronavirus infodemic demands effective methods to detect fake news. To this end, we propose a method to infer news credibility from hashtags involved in news dissemination on social media, motivated by the tight connection between hashtags and news credibility observed in our empirical analyses. We first introduce a new graph that captures all (direct and \textit{indirect}) relationships among hashtags. Then, a language-independent semi-supervised algorithm is developed to predict fake news based on this constructed graph. This study first investigates the indirect relationship among hashtags; the proposed approach can be extended to any homogeneous graph to capture a comprehensive relationship among nodes. Language independence opens the proposed method to multilingual fake news detection. Experiments conducted on two real-world datasets demonstrate the effectiveness of our approach in identifying fake news, especially at an \textit{early} stage of propagation