@article{GladkayagrosseDeters2023,
  author    = {Gladkaya, Margarita and große Deters, Fenne},
  title     = {Capturing the self-others dichotomy of social media use},
  series = {Information \& management : the international journal of information systems applications},
  volume    = {61},
  journal   = {Information \& management : the international journal of information systems applications},
  number    = {1},
  publisher = {Elsevier},
  address   = {Amsterdam},
  issn      = {0378-7206},
  doi       = {10.1016/j.im.2023.103899},
  pages     = {12},
  year      = {2023},
  abstract  = {Focusing on the passive use of Instagram, we apply the affordance perspective to deeply explore its use and use-related outcomes. In the qualitative study, we uncover the affordances of focal social media features. Two distinct groups of affordances (self- and others-oriented) emerge. Following the grounded theory methodology, we develop the affordances-actualizations-outcomes model, explaining how immediate goals associated with features translate into outcomes. In the quantitative study, we test the model by applying structural equation modeling. Our findings confirm that actualizations of self- and others-oriented affordances are associated with distinct outcomes: social connectedness, positive affect, and overall satisfaction with Instagram experience.},
  language  = {en}
}
@article{SmithVasishth2020,
  author    = {Smith, Garrett and Vasishth, Shravan},
  title     = {A principled approach to feature selection in models of sentence processing},
  series = {Cognitive science : a multidisciplinary journal of anthropology, artificial intelligence, education, linguistics, neuroscience, philosophy, psychology ; journal of the Cognitive Science Society},
  volume    = {44},
  journal   = {Cognitive science : a multidisciplinary journal of anthropology, artificial intelligence, education, linguistics, neuroscience, philosophy, psychology ; journal of the Cognitive Science Society},
  number    = {12},
  publisher = {Wiley},
  address   = {Hoboken},
  issn      = {0364-0213},
  doi       = {10.1111/cogs.12918},
  pages     = {25},
  year      = {2020},
  abstract  = {Among theories of human language comprehension, cue-based memory retrieval has proven to be a useful framework for understanding when and how processing difficulty arises in the resolution of long-distance dependencies. Most previous work in this area has assumed that very general retrieval cues like [+subject] or [+singular] do the work of identifying (and sometimes misidentifying) a retrieval target in order to establish a dependency between words. However, recent work suggests that general, handpicked retrieval cues like these may not be enough to explain illusions of plausibility (Cunnings \& Sturt, 2018), which can arise in sentences like The letter next to the porcelain plate shattered. Capturing such retrieval interference effects requires lexically specific features and retrieval cues, but handpicking the features is hard to do in a principled way and greatly increases modeler degrees of freedom. To remedy this, we use well-established word embedding methods for creating distributed lexical feature representations that encode information relevant for retrieval using distributed retrieval cue vectors. We show that the similarity between the feature and cue vectors (a measure of plausibility) predicts total reading times in Cunnings and Sturt's eye-tracking data. The features can easily be plugged into existing parsing models (including cue-based retrieval and self-organized parsing), putting very different models on more equal footing and facilitating future quantitative comparisons.},
  language  = {en}
}