From words to connections: Word use similarity as an honest signal conducive to employees' digital communication

• URL: http://arxiv.org/abs/2111.06133v1
• Date: Thu, 11 Nov 2021 10:32:33 GMT
• Title: From words to connections: Word use similarity as an honest signal conducive to employees' digital communication
• Authors: A. Fronzetti Colladon, J. Saint-Charles, P. Mongeau
• Abstract summary: We analyse the communication of close to 1600 employees, interacting on the intranet communication forum of a large company. We find that word use similarity is the main driver of interaction, much more than other language characteristics or similarity in network position. Our results suggest carefully choosing the language according to the target audience and have practical implications for both company managers and online community administrators.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Bringing together considerations from three research trends (honest signals of collaboration, socio-semantic networks and homophily theory), we hypothesise that word use similarity and having similar social network positions are linked with the level of employees' digital interaction. To verify our hypothesis, we analyse the communication of close to 1600 employees, interacting on the intranet communication forum of a large company. We study their social dynamics and the 'honest signals' that, in past research, proved to be conducive to employees' engagement and collaboration. We find that word use similarity is the main driver of interaction, much more than other language characteristics or similarity in network position. Our results suggest carefully choosing the language according to the target audience and have practical implications for both company managers and online community administrators. Understanding how to better use language could, for example, support the development of knowledge sharing practices or internal communication campaigns.

Related papers

• GOMA: Proactive Embodied Cooperative Communication via Goal-Oriented Mental Alignment [72.96949760114575]
  We propose a novel cooperative communication framework, Goal-Oriented Mental Alignment (GOMA) GOMA formulates verbal communication as a planning problem that minimizes the misalignment between parts of agents' mental states that are relevant to the goals. We evaluate our approach against strong baselines in two challenging environments, Overcooked (a multiplayer game) and VirtualHome (a household simulator)
  arXiv  Detail & Related papers  (2024-03-17T03:52:52Z)
• Exploring Embeddings for Measuring Text Relatedness: Unveiling Sentiments and Relationships in Online Comments [1.7230140898679147]
  This paper investigates sentiment and semantic relationships among comments across various social media platforms. It uses word embeddings to analyze components in sentences and documents. Our analysis will enable a deeper understanding of the interconnectedness of online comments and will investigate the notion of the internet functioning as a large interconnected brain.
  arXiv  Detail & Related papers  (2023-09-15T04:57:23Z)
• Less Data, More Knowledge: Building Next Generation Semantic Communication Networks [180.82142885410238]
  We present the first rigorous vision of a scalable end-to-end semantic communication network. We first discuss how the design of semantic communication networks requires a move from data-driven networks towards knowledge-driven ones. By using semantic representation and languages, we show that the traditional transmitter and receiver now become a teacher and apprentice.
  arXiv  Detail & Related papers  (2022-11-25T19:03:25Z)
• Co-Located Human-Human Interaction Analysis using Nonverbal Cues: A Survey [71.43956423427397]
  We aim to identify the nonverbal cues and computational methodologies resulting in effective performance. This survey differs from its counterparts by involving the widest spectrum of social phenomena and interaction settings. Some major observations are: the most often used nonverbal cue, computational method, interaction environment, and sensing approach are speaking activity, support vector machines, and meetings composed of 3-4 persons equipped with microphones and cameras, respectively.
  arXiv  Detail & Related papers  (2022-07-20T13:37:57Z)
• Interpretation of Emergent Communication in Heterogeneous Collaborative Embodied Agents [83.52684405389445]
  We introduce the collaborative multi-object navigation task CoMON. In this task, an oracle agent has detailed environment information in the form of a map. It communicates with a navigator agent that perceives the environment visually and is tasked to find a sequence of goals. We show that the emergent communication can be grounded to the agent observations and the spatial structure of the 3D environment.
  arXiv  Detail & Related papers  (2021-10-12T06:56:11Z)
• A Neural Network-Based Linguistic Similarity Measure for Entrainment in Conversations [12.052672647509732]
  Linguistic entrainment is a phenomenon where people tend to mimic each other in conversation. Most of the current similarity measures are based on bag-of-words approaches. We propose to use a neural network model to perform the similarity measure for entrainment.
  arXiv  Detail & Related papers  (2021-09-04T19:48:17Z)
• Few-shot Language Coordination by Modeling Theory of Mind [95.54446989205117]
  We study the task of few-shot $textitlanguage coordination$. We require the lead agent to coordinate with a $textitpopulation$ of agents with different linguistic abilities. This requires the ability to model the partner's beliefs, a vital component of human communication.
  arXiv  Detail & Related papers  (2021-07-12T19:26:11Z)
• Characterizing English Variation across Social Media Communities with BERT [9.98785450861229]
  We analyze two months of English comments in 474 Reddit communities. The specificity of different sense clusters to a community, combined with the specificity of a community's unique word types, is used to identify cases where a social group's language deviates from the norm. We find that communities with highly distinctive language are medium-sized, and their loyal and highly engaged users interact in dense networks.
  arXiv  Detail & Related papers  (2021-02-12T23:50:57Z)
• Pow-Wow: A Dataset and Study on Collaborative Communication in Pommerman [12.498028338281625]
  In multi-agent learning, agents must coordinate with each other in order to succeed. For humans, this coordination is typically accomplished through the use of language. We construct Pow-Wow, a new dataset for studying situated goal-directed human communication. We analyze the types of communications which result in effective game strategies, annotate them accordingly, and present corpus-level statistical analysis of how trends in communications affect game outcomes.
  arXiv  Detail & Related papers  (2020-09-13T07:11:37Z)
• Emergence of Pragmatics from Referential Game between Theory of Mind Agents [64.25696237463397]
  We propose an algorithm, using which agents can spontaneously learn the ability to "read between lines" without any explicit hand-designed rules. We integrate the theory of mind (ToM) in a cooperative multi-agent pedagogical situation and propose an adaptive reinforcement learning (RL) algorithm to develop a communication protocol.
  arXiv  Detail & Related papers  (2020-01-21T19:37:33Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.