# Health Misinformation in Social Networks: A Survey of Information Technology Approaches
## 4 Content-Based Methods
### Text Representations  
Text representations address the fundamental problem of converting unstructured documents to mathematically computable forms, usually in the form of vectors. Commonly used approaches include the bag-of-words (BoW) approach, which weights the frequency of a word in a document [30; 87], and the term frequency-inverse document frequency (TF-IDF) approach, which keeps the appearances of words in a document by the fraction \begin{table}
\begin{tabular}{l l l l} \hline \hline
**Name** & **Manual/Automatic** & **API** & **Themes** \\ \hline Snopes & Manual & - & Politics, health, social issues \\ \hline Media Bias/Fact-Check & Manual & - & Science, politics \\ \hline Politifact & Manual & ✓ & Politics, health \\ \hline The Washington Post & Manual & - & Politics, technology, lifestyle, word, sports, health, well being, science \\ \hline Full Fact & Manual and soon & ✓ & Health, economy, crime, law, education, Europe \\ \hline FactCheck & Manual & - & Politics, science, health \\ \hline LeadStories & Manual & - & Politics, health \\ \hline Claimbuster & Automatic & ✓ & General \\ \hline Emergent & Manual & - & Culture, business, politics, viral \\ \hline TruthOrFiction & Manual & - & Politics, health, nature \\ \hline Google Fact-Check & Manual and automatic & ✓ & General \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Fact-checking tools. of documents in the corpus that contain the word [30; 87; 88; 89]. Approaches using n-grams, which are based on contiguous sequences of n 'items' (words, letters, syllables, phonemes, etc. ), are commonly used even in the context of medical fake news detection [89]. More modern approaches use a latent space to create text embeddings over the content of the articles. Embeddings provide a way to translate a high-dimensional vector into a relatively low-dimensional one, placing semantically similar inputs close together in the embedding space. Usually, embeddings are learned over big corpora and datasets so that they can be reused across models, domains, and tasks. It has been shown that the effectiveness of word embeddings in encapsulating the semantic, syntactic, and context features affects the performance of the classification models [90]. Text embeddings can be divided into non-contextual and contextual ones. Non-contextual ones, like word2 vec [91], GloVe [92], and FastText [93], learn word associations from a large corpus of text but do not capture information about the context in which they appear. They provide a single global representation for every word, even if words can be ambiguous and have various meanings. For example, in the context of medical fake news, the authors in [83] use the GloVe pre-trained model to classify COVID-19 news by using various neural network architectures. Contextual embeddings assign to each word a representation based on its context. In this way, they can capture uses of words across varied contexts and can encode knowledge that can be cross-lingual. In [90], the authors evaluate non-contextual and contextual text-embedding approaches over various fake news datasets and showcase the importance of contextual embeddings. A large number of approaches that leverage latent features use pre-trained language models based on the popular BERT model [46] and its derivatives (BART [51], RoBERTa [94], distilBERT [95], Albert [96], XLNet [97], etc.). Such models can be fine-tuned over a collection of fake and valid news for classification reasons, as is the case of FakeBERT [98], and offer an exceptional performance for the task at hand. Furthermore, they can be trained over datasets of a specific domain in order to identify the terminology, constructs, and peculiarities of this domain. An example of a pre-trained representation model in the medical domain is BioBert [99], a BERT model for Biomedical Text Mining. This is the first domain-specific language representation model pre-trained on large-scale biomedical corpora (PubMed abstracts and PMC full-text articles).