Helpful or Hierarchical? Predicting the Communicative Strategies of Chat Participants, and their Impact on Success (2020)

Abstract: When interacting with each other, we motivate, advise, inform, show love or power towards our peers. However, the way we interact may also hold some indication on how successful we are, as people often try to help each other to achieve their goals. We study the chat interactions of thousands of aspiring entrepreneurs who discuss and develop business models. We manually annotate a set of about 5,500 chat interactions with four dimensions of interaction styles (motivation, cooperation, equality, advice). We find that these styles can be reliably predicted, and that the communication styles can be used to predict a number of indices of business success. Our findings indicate that successful communicators are also successful in other domains.

## introduction people are social beings who communicate their feelings, emotions, thoughts, ideas, etc. through verbal and non-verbal interactions. based on these interactions, we build relationships, and these relationships, in turn, help create and maintain a network of peers. peers in a network cooperate with each other, help each other to learn, and exchange ideas. however, they also compete for the same resources @xcite , not least attention. peer networks are particularly important for innovation and entrepreneurship (gonzalez-uribe and leatherbee, 2017), as they produce an active exchange of ideas. people are usually assumed to be altruistic in networks like online social forums. they cooperate with and help one another with answers, advice, and ideas. the motivations behind helping a peer include, but are not limited to, getting pure pleasure from helping, self-advancement, building a reputation, developing relationships, or sheer entertainment @xcite . when people interact with each other, their interactions vary along various communicative styles, such as showing cooperativeness, equality, business orientation, etc. @xcite . varying these communication styles provides tools to achieve communicative goals. for example, someone trying to build a reputation will tend to use a more cooperative style. someone who tries to be helpful may use more words of advice in their interactions. the usage of relationshipestablishing styles is more prevalent in certain personalities @xcite and in specific settings. business-oriented people communicate more independence, tolerance of ambiguity, risk-taking propensity, innovativeness, and leadership qualities @xcite . the impact of these styles is, therefore, an essential factor in text analysis. however, due to their complex, decentralized nature, these communication styles have been studied very little in nlp. cooperativeness is more than just a few keywordsit includes a whole inventory of communicative tools. this property makes it harder to annotate and predict. part of the reason is the lack of adequate corpora. we provide such a corpus and report encouraging results for the above styles. contributions we introduce a new task, predicting the communicative strategies of interlocutors in a real-life setting, and provide a new, multiplyannotated data set of 5k+ instances. we find that the various communicative dimensions can be efficiently predicted. additional tests suggest that the communicative strategy of a person is somewhat predictive of their business success. ## data our ultimate goal is to predict the communicative styles and strategies of aspiring entrepreneurs in an online peer network. the initial corpus is part of a large-scale social science experiment that involved around 5,000 entrepreneurs from 49 african countries @xcite . after completing an online business course, those entrepreneurs interacted in groups of sixty through an internet platform for about two and a half months, resulting in approximately 140,000 chat interactions. besides the chat interactions, the original dataset contains background information about the speakers (country of origin, educational background, age, gender, etc.). all the participants submitted business proposals, which were evaluated by a panel to assess their potential. the original experimental setup was designed to assess how communication among peers affects innovation and entrepreneurship. @xcite therefore already applied nlp techniques for semantic analysis of the interactions. they also manually annotated other indicators, i.e., businessrelatedness, sentiment, and target audience (i.e., one or several people) on a subset of 10k sentences. they trained classifiers on this data to infer these labels for all remaining 130k instances in the corpus. this dataset provides a perfect starting point for our goals. the first step to address our goal involves annotating speech styles on several interactions among the participants. we work on the same subset of previously annotated data, and add our own annotations to enrich the data further. ## annotating communication styles we sample around 5,500 chat interactions (mostly in english language with traces of other lan-guage(s)) which were previously annotated for business-relatedness, sentiment, and audience, and annotate the four communication styles: ## experiments and results we want to predict four styles of interactions (cooperative, motivational, advice, equality), and three subsequent indicators of business success: (1) whether the person owns a business (has busi-ness), (2) whether someone has ever owned a business (business ever) and (3) whether they submitted a business proposal to win funding to start a business (business proposal). we use (1) an svm classifier with rbf kernel (effective in @xcite ) to predict both the communicative styles and the business success indicators, and (2) a multitask learning (mtl) convolutional neural network to predict the business success indicators. we divide our annotated dataset into 80-20 stratified train-test splits for predicting communicative styles. for predicting indicators of business success, we use 500 randomly selected instances as test and the rest as training data. ## related work there have been a few studies analyzing language usage when people communicate. for example, researchers have studied power (or hierarchical) relationships in online communities @xcite , emails @xcite , and social networks @xcite . some have studied how roles of wikipedia editors affect their success @xcite . @xcite an-alyze politeness in online forums using structural and linguistic features derived from the communications between two individuals. @xcite develop an algorithm to predict user influence levels in online communities. @xcite characterize interactions between people with dimensions and produce a dataset annotating dimensions on tv scripts. @xcite annotate business relevance and sentiment on online chat interactions among aspiring entrepreneurs. in contrast, we annotate the communicative styles cooperativeness, motivational, advice and equality on chat interactions between young aspiring entrepreneurs, and develop machine learning systems to automatically predict these styles and indicators of business success for the participants. ## conclusions we present a data set of 5k+ instances annotated with four communication styles which can effectively be predicted. these communicative styles also influence people's business success. our results and data set open up interesting new avenues to study the effects of people's communicative strategies on their business success.