Curating Cyberbullying Datasets: a Human-AI Collaborative Approach.

Cyberbullying is the use of digital communication tools and spaces to inflict physical, mental, or emotional distress. This serious form of aggression is frequently targeted at, but not limited to, vulnerable populations. A common problem when creating machine learning models to identify cyberbullying is the availability of accurately annotated, reliable, relevant, and diverse datasets. Datasets intended to train models for cyberbullying detection are typically annotated by human participants, which can introduce the following issues: (1) annotator bias, (2) incorrect annotation due to language and cultural barriers, and (3) the inherent subjectivity of the task can naturally create multiple valid labels for a given comment. The result can be a potentially inadequate dataset with one or more of these overlapping issues. We propose two machine learning approaches to identify and filter unambiguous comments in a cyberbullying dataset of roughly 19,000 comments collected from YouTube that was initially annotated using Amazon Mechanical Turk (AMT). Using consensus filtering methods, comments were classified as unambiguous when an agreement occurred between the AMT workers' majority label and the unanimous algorithmic filtering label. Comments identified as unambiguous were extracted and used to curate new datasets. We then used an artificial neural network to test for performance on these datasets. Compared to the original dataset, the classifier exhibits a large improvement in performance on modified versions of the dataset and can yield insight into the type of data that is consistently classified as bullying or non-bullying. This annotation approach can be expanded from cyberbullying datasets onto any classification corpus that has a similar complexity in scope.

Distance-dependent differences in synapse number and AMPA receptor expression in hippocampal CA1 pyramidal neurons.

The ability of synapses throughout the dendritic tree to influence neuronal output is crucial for information processing in the brain. Synaptic potentials attenuate dramatically, however, as they propagate along dendrites toward the soma. To examine whether excitatory axospinous synapses on CA1 pyramidal neurons compensate for their distance from the soma to counteract such dendritic filtering, we evaluated axospinous synapse number and receptor expression in three progressively distal regions: proximal and distal stratum radiatum (SR), and stratum lacunosum-moleculare (SLM). We found that the proportion of perforated synapses increases as a function of distance from the soma and that their AMPAR, but not NMDAR, expression is highest in distal SR and lowest in SLM. Computational models of pyramidal neurons derived from these results suggest that they arise from the compartment-specific use of conductance scaling in SR and dendritic spikes in SLM to minimize the influence of distance on synaptic efficacy.