text: Existing studies of how information diffuses across social networks have thus far concentrated on analysing and recovering the spread of deterministic innovations such as URLs, hashtags, and group membership. However investigating how mentions of real-world entities appear and spread has yet to be explored, largely due to the computationally intractable nature of performing large-scale entity extraction. In this paper we present, to the best of our knowledge, one of the first pieces of work to closely examine the diffusion of named entities on social media, using Reddit as our case study platform. We first investigate how named entities can be accurately recognised and extracted from discussion posts. We then use these extracted entities to study the patterns of entity cascades and how the probability of a user adopting an entity (i.e. mentioning it) is associated with exposures to the entity. We put these pieces together by presenting a parallelised diffusion model that can forecast the probability of entity adoption, finding that the influence of adoption between users can be characterised by their prior interactions -- as opposed to whether the users propagated entity-adoptions beforehand. Our findings have important implications for researchers studying influence and language, and for community analysts who wish to understand entity-level influence dynamics.
  @cite_35: People tweet more than 100 Million times daily, yielding a noisy, informal, but sometimes informative corpus of 140-character messages that mirrors the zeitgeist in an unprecedented manner. The performance of standard NLP tools is severely degraded on tweets. This paper addresses this issue by re-building the NLP pipeline beginning with part-of-speech tagging, through chunking, to named-entity recognition. Our novel T-ner system doubles F1 score compared with the Stanford NER system. T-ner leverages the redundancy inherent in tweets to achieve this performance, using LabeledLDA to exploit Freebase dictionaries as a source of distant supervision. LabeledLDA outperforms co-training, increasing F1 by 25 over ten common entity types. Our NLP tools are available at: http: github.com aritter twitter_nlp
  @cite_37: 
  @cite_14: Named entity recognition (NER) systems trained on newswire perform very badly when tested on Twitter. Signals that were reliable in copy-edited text disappear almost entirely in Twitter’s informal chatter, requiring the construction of specialized models. Using wellunderstood techniques, we set out to improve Twitter NER performance when given a small set of annotated training tweets. To leverage unlabeled tweets, we build Brown clusters and word vectors, enabling generalizations across distributionally similar words. To leverage annotated newswire data, we employ an importance weighting scheme. Taken all together, we establish a new state-of-the-art on two common test sets. Though it is wellknown that word representations are useful for NER, supporting experiments have thus far focused on newswire data. We emphasize the effectiveness of representations on Twitter NER, and demonstrate that their inclusion can improve performance by up to 20 F1.
  @cite_18: Twitter provides access to large volumes of data in real time, but is notoriously noisy, hampering its utility for NLP. In this paper, we target out-of-vocabulary words in short text messages and propose a method for identifying and normalising ill-formed words. Our method uses a classifier to detect ill-formed words, and generates correction candidates based on morphophonemic similarity. Both word similarity and context are then exploited to select the most probable correction candidate for the word. The proposed method doesn't require any annotations, and achieves state-of-the-art performance over an SMS corpus and a novel dataset based on Twitter.
  @cite_7: We present two new NER datasets for Twitter; a manually annotated set of 1,467 tweets (κ = 0.942) and a set of 2,975 expert-corrected, crowdsourced NER annotated tweets from the dataset described in (2010). In our experiments with these datasets, we observe two important points: (a) language drift on Twitter is significant, and while off-the-shelf systems have been reported to perform well on in-sample data, they often perform poorly on new samples of tweets, (b) state-of-the-art performance across various datasets can be obtained from crowdsourced annotations, making it more feasible to ”catch up” with language drift.
  @cite_6: #Microposts2015, the 5th workshop on 'Making Sense of Microposts', is summarised by the sub-theme: 'big things come in small packages'. The workshop was borne out of research we were each carrying out as microblogging platforms became increasingly popular, and their value as a publishing platform and the data generated as a result began to be recognised. This phenomenon continues to grow, as microblogs provide a low-effort means of publishing information within private, but moreso public, fora, giving a voice to all in all arenas.
  @cite_2: The challenges of Named Entities Recognition (NER) for tweets lie in the insufficient information in a tweet and the unavailability of training data. We propose to combine a K-Nearest Neighbors (KNN) classifier with a linear Conditional Random Fields (CRF) model under a semi-supervised learning framework to tackle these challenges. The KNN based classifier conducts pre-labeling to collect global coarse evidence across tweets while the CRF model conducts sequential labeling to capture fine-grained information encoded in a tweet. The semi-supervised learning plus the gazetteers alleviate the lack of training data. Extensive experiments show the advantages of our method over the baselines as well as the effectiveness of KNN and semi-supervised learning.
  @cite_15: 
  @cite_34: We experiment with using different sources of distant supervision to guide unsupervised and semi-supervised adaptation of part-of-speech (POS) and named entity taggers (NER) to Twitter. We show that a particularly good source of not-so-distant supervision is linked websites. Specifically, with this source of supervision we are able to improve over the state-of-the-art for Twitter POS tagging (89.76 accuracy, 8 error reduction) and NER (F1=79.4 , 10 error reduction).
summary: NER for social media content is however difficult, leading to much work, including general approaches @cite_35 , topic-specific approaches @cite_2 , adapting from known genres @cite_34 ; these are driven by and evaluated in multiple recent shared tasks @cite_6 @cite_37 . The task is generally cast as a domain adaptation problem from newswire data, integrating the two kinds of data for training @cite_14 or including a lexical normalisation step @cite_18 to shift text to territory more familiar to existing models and methods. Major challenges are that NEs mentioned in tweets change over time @cite_7 , and that diversity of context makes NER more difficult @cite_15 . This paper addresses NER without using large amounts of labelled in-domain data, in order to track entity propagation at scale.