Social media are digitalizing massive amounts of users' cognitions in terms of timelines and emotional content. Such Big Data opens unprecedented opportunities for investigating cognitive phenomena like perception, personality, and information diffusion but requires suitable interpretable frameworks. Since social media data come from users' minds, worthy candidates for this challenge are cognitive networks, models of cognition giving structure to mental conceptual associations. This work outlines how cognitive network science can open new, quantitative ways for understanding cognition through online media like: (i) reconstructing how users semantically and emotionally frame events with contextual knowledge unavailable to machine learning, (ii) investigating conceptual salience/prominence through knowledge structure in social discourse; (iii) studying users' personality traits like openness-to-experience, curiosity, and creativity through language in posts; (iv) bridging cognitive/emotional content and social dynamics via multilayer networks comparing the mindsets of influencers and followers. These advancements combine cognitive-, network- and computer science to understand cognitive mechanisms in both digital and real-world settings but come with limitations concerning representativeness, individual variability, and data integration. Such aspects are discussed along with the ethical implications of manipulating sociocognitive data. In the future, reading cognitions through networks and social media can expose cognitive biases amplified by online platforms and relevantly inform policy-making, education, and markets about complex cognitive trends.

https://onlinelibrary.wiley.com/doi/pdf/10.1111/tops.12551

Cognitive Network Science for Understanding Online Social Cognitions: A Brief Review.

Language production is largely a matter of words which, in the case of access problems, can be searched for in an external resource (lexicon, thesaurus). When accessing the resource, the user provides her momentarily available knowledge concerning the target and the resource-powered system responds with the best guess(es) it can make given this input. As tip-of-the-tongue studies have shown, people always have some knowledge concerning the target (meaning fragments, number of syllables, ...) even if its precise or complete form is eluding them. We will show here how to tap on this knowledge to build a resource likely to help authors (speakers/writers) to overcome the Tip-of-the-Tongue (ToT) problem. Yet, before doing so we need a better understanding of the various kinds of knowledge people have when looking for a word. To this end, we asked crowd workers to provide some cues to describe a given target and to specify then how each one of them relates to it, in the hope that this could help others to find the elusive word. Next, we checked how well a given search strategy worked when being applied to differently built lexical networks. The results showed quite dramatic differences, which is not really surprising. After all, different networks are built for different purposes; hence each one of them is more or less well suited for a given task. What was more surprising though is the fact that the relational information given by the users did not allow us to find the elusive Manuscript

https://hal.archives-ouvertes.fr/hal-03168850/document

Comparison of Different Lexical Resources With Respect to the Tip-of-the-Tongue Problem

To search for a word in a dictionary without a proper index is like looking for an address in a city without a decent map. Abstract Dictionaries are repositories of knowledge concerning words. While 1 readers are mostly concerned with meanings, writers are generally more concerned 2 with word forms expressing meanings (lemma). I will focus here on this latter task: 3 building a tool to help authors to find the word they are looking for, word they may 4 know but whose form is eluding them. Put differently, my goal is to build a resource 5 helping authors to overcome the Tip-of-the-Tongue problem (ToT). Obviously, in 6 order to access a word, it must be stored somewhere (brain, resource). Yet this is 7 far from sufficient. Access may depend on many other factors than storage of word 8 forms: organization of the dictionary (index), the user's cognitive state, i.e. available 9 knowledge at the onset of search, the distance between the source-and the target-10 word (direct neighbor or not) , the knowledge of the relationship between the two, 11 etc. I will try to provide evidence for the claim that (word) storage does not guar-12 antee access. To this end I will compare a well-known lexical resource, WordNet 13 (WN), to an equivalent one, but bootstrapped from Wikipedia (WiPe). While both 14 versions contain basically the same set of words, the latter contains many more 15 (syntagmatic) links than WN. This is probably the reason why WiPe outperforms 16 WN. In the last two sections I will explain under what conditions WN is suitable 17 for word access, and what it might take to go beyond the limitations of this famous 18 resource.

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Eureka! A Simple Solution to the Complex ‘Tip-of-the-Tongue’-Problem

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Eureka! A Simple Solution to the Complex ‘Tip-of-the-Tongue’-Problem